Artificial Intelligence Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App

In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.

Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write your Algorithm
  • Step 7: Test Your Algorithm

Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels
In [33]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))

There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

In [34]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))

There are 13233 total human images.

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [35]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()

Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [36]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer: Per the executed code and outputs in the cell below

  • 99% of the first 100 images in human files detected human faces
  • 11% of the first 100 images in dog files detected human faces
In [37]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
num_humans = float(len(human_files_short))
num_dogs = float(len(dog_files_short))
face_detector = np.vectorize(face_detector)
human_face_predictions = face_detector(human_files_short)
dog_face_predictions = face_detector(dog_files_short)
perc_human_faces_in_human_files = float(sum(human_face_predictions))/num_humans
perc_human_faces_in_dog_files = float(sum(dog_face_predictions))/num_dogs
print('[ info ] Percentage of human files detected as human faces: {0:.0%}'.format(perc_human_faces_in_human_files))
print('[ info ] Percentage of dog files detected as human faces: {0:.0%}'.format(perc_human_faces_in_dog_files))

[ info ] Percentage of human files detected as human faces: 99%
[ info ] Percentage of dog files detected as human faces: 11%

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer:

Its reasonable because there is always a chance that users will intentionally or unintentionally try to enter unclear faces. They could enter unclear photos intentionally while testing the robustness of the application for example through so-called negative testing. They could enter unclear photos unintentionally due to for example the quality of the device being to take a photo, the conditions existing at the time the photo is taken, for example poor light or photo taken while in motion etc.

However I think there are potential ways to detect humans in images that does not necessitate an image with a clearly presented face. In their paper, FaceNet: A unified Embedding for Face Recognition and Clustering, Shroff, Kalenichenko and Philbin introduce 'the concept of harmonic embeddings, and a harmonic triplet loss, which describe different versions of face embeddings (produced by different networks) that are compatible to each other and allow for direct comparison between each other'. Their method uses a deep convolutional network to learn 128 byte embeddings using so called triplets (an anchor, positive and negative images). The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity. Simailar faces have small distances and different faces have large distances. I think this method can be generalised so that unclear images of faces of humans have larger distances from clear trained embeddings of human faces.

Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [38]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
print(ResNet50_model)

<keras.engine.training.Model object at 0x0000024141071FD0>

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

In [39]:
from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [40]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [41]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151))

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer: The output from executing the code below show that the percentage of dogs detected in human files is 1% and the percentage of dogs detected in dog files is 100%.

In [42]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
dog_detector = np.vectorize(dog_detector)
num_humans = float(len(human_files_short))
num_dogs = float(len(dog_files_short))
#face_detector = np.vectorize(face_detector)
dog_predictions_humans = dog_detector(human_files_short)
dog_predictions_dogs = dog_detector(dog_files_short)
perc_dogs_in_human_files = float(sum(dog_predictions_humans))/num_humans
perc_dogs_in_dog_files = float(sum(dog_predictions_dogs))/num_dogs
print('[ info ] Percentage of dogs detected in human files : {0:.0%}'.format(perc_dogs_in_human_files))
print('[ info ] Percentage of dog detected in dog files: {0:.0%}'.format(perc_dogs_in_dog_files))

[ info ] Percentage of dogs detected in human files : 1%
[ info ] Percentage of dog detected in dog files: 100%

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

In [43]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
print(train_tensors.shape)

100%|██████████████████████████████████████████████████████████████████████████████| 6680/6680 [01:20<00:00, 82.52it/s]
100%|████████████████████████████████████████████████████████████████████████████████| 835/835 [00:12<00:00, 66.32it/s]
100%|████████████████████████████████████████████████████████████████████████████████| 836/836 [00:12<00:00, 66.80it/s]

(6680, 224, 224, 3)

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Sample CNN

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer: I started off with the above sample network and through, partly trial and error and research, came up with the following architecture for my CNN:

  • Three blocks of Conv2D and MaxPooling2D with RELU activation and Batch Normalization;
  • 2 Fully Connected Layers with the first Fully Connected having RELU and Dropout regularization and the second being a softmax layer outputting the 133 classes of dogs;
  • In total this means my CNN has 8 layers;
  • From the models I have run and seen online as well as research it looks like for image classification tasks Convolutional Neural Networks only start getting clear images from about layer 3 or 4;
  • My motivation for Batch Normalization came from a paper by Ioffe and Szegedy (Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift);
  • In that paper they show evidence that Batch Normalization achieves the same accuracy with 14 times fewer training steps therefore significantly speeding up training;
  • They also argued and showed that Batch Norm is a form of regularization so I didnt add Dropout to my first 6 layers;
  • Also 2 fully connected layers seems the norm in practice. I eventually settled after a couple or runs;
  • I trained my architecture with 5 epochs and achieved an accuracy of 1.67% accuracy on the test set.
In [44]:
from keras.layers import Conv2D, ZeroPadding2D, MaxPooling2D, GlobalAveragePooling2D, Input, Activation, BatchNormalization
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential, Model

model = Sequential()

# INPUT SHAPE
input_shape = train_tensors.shape[1:]

### TODO: Define your architecture.
# CONV -> BATCHNORMALIZATION -> RELU -> BatchNormalization
model.add(Conv2D(16, (7,7), input_shape=input_shape))
model.add(Activation('relu'))
model.add(BatchNormalization(axis=3))

# MAXPOOL
model.add(MaxPooling2D(pool_size=(2, 2)))

# CONV -> BATCHNORMALIZATION -> RELU -> BatchNormalization
model.add(Conv2D(32, (5,5)))
model.add(Activation('relu'))
model.add(BatchNormalization(axis = 3))

# MAXPOOL
model.add(MaxPooling2D(pool_size=(2, 2)))

# CONV -> BATCHNORMALIZATION -> RELU -> BatchNormalization
model.add(Conv2D(64, (3,3)))
model.add(Activation('relu'))
model.add(BatchNormalization(axis = 3))

#MAXPOOL
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

# FLATTEN X + 2 FULLCONNECTED LAYERS (one with RELU and the other softmax)
model.add(Flatten())
model.add(Dense(500))
model.add(Activation('relu'))
model.add(Dropout(0.2)) 
model.add(Dense(133))
model.add(Activation('softmax'))

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1038 (Conv2D)         (None, 218, 218, 16)      2368      
_________________________________________________________________
activation_1141 (Activation) (None, 218, 218, 16)      0         
_________________________________________________________________
batch_normalization_1038 (Ba (None, 218, 218, 16)      64        
_________________________________________________________________
max_pooling2d_50 (MaxPooling (None, 109, 109, 16)      0         
_________________________________________________________________
conv2d_1039 (Conv2D)         (None, 105, 105, 32)      12832     
_________________________________________________________________
activation_1142 (Activation) (None, 105, 105, 32)      0         
_________________________________________________________________
batch_normalization_1039 (Ba (None, 105, 105, 32)      128       
_________________________________________________________________
max_pooling2d_51 (MaxPooling (None, 52, 52, 32)        0         
_________________________________________________________________
conv2d_1040 (Conv2D)         (None, 50, 50, 64)        18496     
_________________________________________________________________
activation_1143 (Activation) (None, 50, 50, 64)        0         
_________________________________________________________________
batch_normalization_1040 (Ba (None, 50, 50, 64)        256       
_________________________________________________________________
max_pooling2d_52 (MaxPooling (None, 25, 25, 64)        0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 25, 25, 64)        0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 40000)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 500)               20000500  
_________________________________________________________________
activation_1144 (Activation) (None, 500)               0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 133)               66633     
_________________________________________________________________
activation_1145 (Activation) (None, 133)               0         
=================================================================
Total params: 20,101,277.0
Trainable params: 20,101,053.0
Non-trainable params: 224.0
_________________________________________________________________

Compile the Model

In [45]:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [23]:
from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 5

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)

Train on 6680 samples, validate on 835 samples
Epoch 1/5
6660/6680 [============================>.] - ETA: 1830s - loss: 6.8579 - acc: 0.0000e+00 - ETA: 1335s - loss: 11.4880 - acc: 0.0000e+00 - ETA: 1167s - loss: 12.7173 - acc: 0.0167     - ETA: 1083s - loss: 13.5675 - acc: 0.0125 - ETA: 1128s - loss: 14.0547 - acc: 0.0100 - ETA: 1081s - loss: 14.2643 - acc: 0.0167 - ETA: 1045s - loss: 14.4645 - acc: 0.0143 - ETA: 1027s - loss: 14.5090 - acc: 0.0125 - ETA: 1007s - loss: 14.6878 - acc: 0.0111 - ETA: 988s - loss: 14.7507 - acc: 0.0150  - ETA: 974s - loss: 14.8750 - acc: 0.0136 - ETA: 963s - loss: 14.9786 - acc: 0.0125 - ETA: 950s - loss: 15.0662 - acc: 0.0115 - ETA: 938s - loss: 15.1414 - acc: 0.0107 - ETA: 928s - loss: 15.1880 - acc: 0.0100 - ETA: 919s - loss: 15.2461 - acc: 0.0094 - ETA: 911s - loss: 15.2974 - acc: 0.0088 - ETA: 902s - loss: 15.3430 - acc: 0.0083 - ETA: 895s - loss: 15.3838 - acc: 0.0079 - ETA: 889s - loss: 15.4205 - acc: 0.0075 - ETA: 882s - loss: 15.4248 - acc: 0.0071 - ETA: 876s - loss: 15.4197 - acc: 0.0091 - ETA: 871s - 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acc: 0.0145 - ETA: 61s - loss: 15.8158 - acc: 0.0146 - ETA: 58s - loss: 15.8119 - acc: 0.0147 - ETA: 55s - loss: 15.8129 - acc: 0.0146 - ETA: 52s - loss: 15.8139 - acc: 0.0146 - ETA: 50s - loss: 15.8148 - acc: 0.0146 - ETA: 47s - loss: 15.8132 - acc: 0.0147 - ETA: 44s - loss: 15.8142 - acc: 0.0146 - ETA: 41s - loss: 15.8126 - acc: 0.0147 - ETA: 38s - loss: 15.8111 - acc: 0.0148 - ETA: 36s - loss: 15.8120 - acc: 0.0148 - ETA: 33s - loss: 15.8130 - acc: 0.0148 - ETA: 30s - loss: 15.8139 - acc: 0.0147 - ETA: 27s - loss: 15.8148 - acc: 0.0147 - ETA: 25s - loss: 15.8158 - acc: 0.0146 - ETA: 22s - loss: 15.8167 - acc: 0.0146 - ETA: 19s - loss: 15.8176 - acc: 0.0145 - ETA: 16s - loss: 15.8185 - acc: 0.0145 - ETA: 13s - loss: 15.8195 - acc: 0.0144 - ETA: 11s - loss: 15.8179 - acc: 0.0145 - ETA: 8s - loss: 15.8188 - acc: 0.0145  - ETA: 5s - loss: 15.8175 - acc: 0.0145 - ETA: 2s - loss: 15.8184 - acc: 0.0144Epoch 00000: val_loss improved from inf to 15.84785, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 984s - loss: 15.8193 - acc: 0.0144 - val_loss: 15.8479 - val_acc: 0.0168
Epoch 2/5
6660/6680 [============================>.] - ETA: 967s - loss: 16.1181 - acc: 0.0000e+00 - ETA: 959s - loss: 16.1181 - acc: 0.0000e+00 - ETA: 946s - loss: 15.8495 - acc: 0.0167     - ETA: 938s - loss: 15.5137 - acc: 0.0375 - ETA: 931s - loss: 15.4734 - acc: 0.0400 - ETA: 931s - loss: 15.5808 - acc: 0.0333 - ETA: 927s - loss: 15.6576 - acc: 0.0286 - ETA: 922s - loss: 15.7151 - acc: 0.0250 - ETA: 920s - loss: 15.7599 - acc: 0.0222 - ETA: 916s - loss: 15.7151 - acc: 0.0250 - ETA: 912s - loss: 15.7518 - acc: 0.0227 - ETA: 911s - loss: 15.7823 - acc: 0.0208 - ETA: 909s - loss: 15.6841 - acc: 0.0269 - ETA: 906s - loss: 15.7151 - acc: 0.0250 - ETA: 902s - loss: 15.7420 - acc: 0.0233 - ETA: 898s - loss: 15.7655 - acc: 0.0219 - ETA: 896s - loss: 15.6440 - acc: 0.0294 - ETA: 893s - loss: 15.6256 - acc: 0.0306 - ETA: 889s - loss: 15.6515 - acc: 0.0289 - ETA: 886s - loss: 15.6748 - acc: 0.0275 - ETA: 883s - loss: 15.6960 - acc: 0.0262 - ETA: 880s - loss: 15.7151 - acc: 0.0250 - ETA: 878s - loss: 15.7327 - 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ETA: 63s - loss: 15.7971 - acc: 0.0189 - ETA: 60s - loss: 15.7981 - acc: 0.0188 - ETA: 58s - loss: 15.7966 - acc: 0.0189 - ETA: 55s - loss: 15.7950 - acc: 0.0190 - ETA: 52s - loss: 15.7961 - acc: 0.0190 - ETA: 49s - loss: 15.7971 - acc: 0.0189 - ETA: 46s - loss: 15.7956 - acc: 0.0190 - ETA: 43s - loss: 15.7966 - acc: 0.0190 - ETA: 40s - loss: 15.7976 - acc: 0.0189 - ETA: 37s - loss: 15.7986 - acc: 0.0188 - ETA: 34s - loss: 15.7971 - acc: 0.0189 - ETA: 31s - loss: 15.7981 - acc: 0.0189 - ETA: 29s - loss: 15.7990 - acc: 0.0188 - ETA: 26s - loss: 15.8000 - acc: 0.0188 - ETA: 23s - loss: 15.7961 - acc: 0.0190 - ETA: 20s - loss: 15.7946 - acc: 0.0191 - ETA: 17s - loss: 15.7931 - acc: 0.0192 - ETA: 14s - loss: 15.7941 - acc: 0.0191 - ETA: 11s - loss: 15.7951 - acc: 0.0191 - ETA: 8s - loss: 15.7960 - acc: 0.0190  - ETA: 5s - loss: 15.7946 - acc: 0.0191 - ETA: 2s - loss: 15.7956 - acc: 0.0191Epoch 00001: val_loss did not improve
6680/6680 [==============================] - 1019s - loss: 15.7965 - acc: 0.0190 - val_loss: 15.9251 - val_acc: 0.0120
Epoch 3/5
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loss: 15.7538 - acc: 0.0219 - ETA: 63s - loss: 15.7550 - acc: 0.0218 - ETA: 60s - loss: 15.7484 - acc: 0.0222 - ETA: 57s - loss: 15.7496 - acc: 0.0221 - ETA: 54s - loss: 15.7482 - acc: 0.0222 - ETA: 52s - loss: 15.7494 - acc: 0.0222 - ETA: 49s - loss: 15.7505 - acc: 0.0221 - ETA: 46s - loss: 15.7517 - acc: 0.0220 - ETA: 43s - loss: 15.7528 - acc: 0.0219 - ETA: 40s - loss: 15.7540 - acc: 0.0219 - ETA: 37s - loss: 15.7551 - acc: 0.0218 - ETA: 34s - loss: 15.7562 - acc: 0.0217 - ETA: 31s - loss: 15.7574 - acc: 0.0217 - ETA: 28s - loss: 15.7585 - acc: 0.0216 - ETA: 26s - loss: 15.7596 - acc: 0.0215 - ETA: 23s - loss: 15.7607 - acc: 0.0215 - ETA: 20s - loss: 15.7593 - acc: 0.0216 - ETA: 17s - loss: 15.7604 - acc: 0.0215 - ETA: 14s - loss: 15.7566 - acc: 0.0217 - ETA: 11s - loss: 15.7577 - acc: 0.0217 - ETA: 8s - loss: 15.7588 - acc: 0.0216  - ETA: 5s - loss: 15.7599 - acc: 0.0215 - ETA: 2s - loss: 15.7609 - acc: 0.0215Epoch 00002: val_loss improved from 15.84785 to 15.78963, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1018s - loss: 15.7572 - acc: 0.0217 - val_loss: 15.7896 - val_acc: 0.0204
Epoch 4/5
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ETA: 64s - loss: 15.7560 - acc: 0.0218 - ETA: 61s - loss: 15.7546 - acc: 0.0219 - ETA: 58s - loss: 15.7532 - acc: 0.0220 - ETA: 55s - loss: 15.7543 - acc: 0.0219 - ETA: 52s - loss: 15.7555 - acc: 0.0218 - ETA: 49s - loss: 15.7516 - acc: 0.0221 - ETA: 46s - loss: 15.7476 - acc: 0.0223 - ETA: 43s - loss: 15.7463 - acc: 0.0224 - ETA: 40s - loss: 15.7474 - acc: 0.0223 - ETA: 38s - loss: 15.7486 - acc: 0.0223 - ETA: 35s - loss: 15.7447 - acc: 0.0225 - ETA: 32s - loss: 15.7459 - acc: 0.0224 - ETA: 29s - loss: 15.7470 - acc: 0.0224 - ETA: 26s - loss: 15.7457 - acc: 0.0225 - ETA: 23s - loss: 15.7468 - acc: 0.0224 - ETA: 20s - loss: 15.7480 - acc: 0.0223 - ETA: 17s - loss: 15.7491 - acc: 0.0223 - ETA: 14s - loss: 15.7478 - acc: 0.0223 - ETA: 11s - loss: 15.7489 - acc: 0.0223 - ETA: 8s - loss: 15.7476 - acc: 0.0224  - ETA: 5s - loss: 15.7463 - acc: 0.0224 - ETA: 2s - loss: 15.7450 - acc: 0.0225Epoch 00003: val_loss improved from 15.78963 to 15.72486, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1029s - loss: 15.7461 - acc: 0.0225 - val_loss: 15.7249 - val_acc: 0.0240
Epoch 5/5
6660/6680 [============================>.] - ETA: 981s - loss: 16.1181 - acc: 0.0000e+00 - ETA: 964s - loss: 15.7151 - acc: 0.0250     - ETA: 962s - loss: 15.8495 - acc: 0.0167 - ETA: 956s - loss: 15.9166 - acc: 0.0125 - ETA: 948s - loss: 15.9569 - acc: 0.0100 - ETA: 945s - loss: 15.9838 - acc: 0.0083 - ETA: 943s - loss: 15.8878 - acc: 0.0143 - ETA: 941s - loss: 15.9166 - acc: 0.0125 - ETA: 938s - loss: 15.8495 - acc: 0.0167 - ETA: 936s - loss: 15.7151 - acc: 0.0250 - ETA: 937s - loss: 15.6785 - acc: 0.0273 - ETA: 934s - loss: 15.7151 - acc: 0.0250 - ETA: 932s - loss: 15.7461 - acc: 0.0231 - ETA: 929s - loss: 15.7610 - acc: 0.0214 - ETA: 926s - loss: 15.7311 - acc: 0.0233 - ETA: 922s - loss: 15.7553 - acc: 0.0219 - ETA: 918s - loss: 15.7767 - acc: 0.0206 - ETA: 916s - loss: 15.7956 - acc: 0.0194 - ETA: 913s - loss: 15.7702 - acc: 0.0211 - ETA: 910s - loss: 15.7750 - acc: 0.0200 - ETA: 908s - loss: 15.7715 - acc: 0.0190 - ETA: 905s - loss: 15.7506 - acc: 0.0205 - ETA: 903s - loss: 15.7316 - 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ETA: 64s - loss: 15.7335 - acc: 0.0236 - ETA: 61s - loss: 15.7322 - acc: 0.0236 - ETA: 58s - loss: 15.7334 - acc: 0.0236 - ETA: 55s - loss: 15.7346 - acc: 0.0235 - ETA: 52s - loss: 15.7359 - acc: 0.0234 - ETA: 49s - loss: 15.7371 - acc: 0.0233 - ETA: 46s - loss: 15.7383 - acc: 0.0233 - ETA: 43s - loss: 15.7395 - acc: 0.0232 - ETA: 40s - loss: 15.7406 - acc: 0.0231 - ETA: 37s - loss: 15.7393 - acc: 0.0232 - ETA: 35s - loss: 15.7405 - acc: 0.0231 - ETA: 32s - loss: 15.7416 - acc: 0.0231 - ETA: 29s - loss: 15.7428 - acc: 0.0230 - ETA: 26s - loss: 15.7440 - acc: 0.0229 - ETA: 23s - loss: 15.7426 - acc: 0.0230 - ETA: 20s - loss: 15.7438 - acc: 0.0229 - ETA: 17s - loss: 15.7449 - acc: 0.0229 - ETA: 14s - loss: 15.7461 - acc: 0.0228 - ETA: 11s - loss: 15.7448 - acc: 0.0229 - ETA: 8s - loss: 15.7459 - acc: 0.0228  - ETA: 5s - loss: 15.7446 - acc: 0.0229 - ETA: 2s - loss: 15.7409 - acc: 0.0231Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 1029s - loss: 15.7396 - acc: 0.0232 - val_loss: 15.7858 - val_acc: 0.0192
Out[23]:
<keras.callbacks.History at 0x1a10f4acf28>

Load the Model with the Best Validation Loss

In [46]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

In [47]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Test accuracy: 1.6746%

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

In [48]:
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
print(train_VGG16.shape)

(6680, 7, 7, 512)

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

In [49]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_3 ( (None, 512)               0         
_________________________________________________________________
dense_9 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229.0
Trainable params: 68,229.0
Non-trainable params: 0.0
_________________________________________________________________

Compile the Model

In [50]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

In [51]:
from keras.callbacks import ModelCheckpoint  

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

Train on 6680 samples, validate on 835 samples
Epoch 1/20
6660/6680 [============================>.] - ETA: 1124s - loss: 15.5212 - acc: 0.0000e+00 - ETA: 226s - loss: 15.2985 - acc: 0.0000e+00  - ETA: 126s - loss: 15.0682 - acc: 0.0056     - ETA: 87s - loss: 14.9593 - acc: 0.0115  - ETA: 67s - loss: 15.0583 - acc: 0.0088 - ETA: 57s - loss: 14.9473 - acc: 0.0100 - ETA: 50s - loss: 14.9510 - acc: 0.0109 - ETA: 44s - loss: 14.9644 - acc: 0.0115 - ETA: 40s - loss: 14.8977 - acc: 0.0138 - ETA: 36s - loss: 14.7990 - acc: 0.0141 - ETA: 33s - loss: 14.7335 - acc: 0.0157 - ETA: 30s - loss: 14.6997 - acc: 0.0154 - ETA: 27s - loss: 14.6781 - acc: 0.0186 - ETA: 25s - loss: 14.5574 - acc: 0.0213 - ETA: 23s - loss: 14.4997 - acc: 0.0245 - ETA: 21s - loss: 14.4107 - acc: 0.0273 - ETA: 20s - loss: 14.3609 - acc: 0.0297 - ETA: 18s - loss: 14.3653 - acc: 0.0286 - ETA: 17s - loss: 14.3359 - acc: 0.0284 - ETA: 16s - loss: 14.3046 - acc: 0.0275 - ETA: 15s - loss: 14.2694 - acc: 0.0300 - ETA: 14s - loss: 14.2151 - acc: 0.0316 - ETA: 14s - loss: 14.1935 - acc: 0.0325 - ETA: 13s - loss: 14.1535 - acc: 0.0333 - ETA: 12s - loss: 14.0637 - acc: 0.0363 - ETA: 12s - loss: 14.0348 - acc: 0.0379 - ETA: 11s - loss: 14.0022 - acc: 0.0394 - ETA: 11s - loss: 13.9766 - acc: 0.0408 - ETA: 10s - loss: 13.9649 - acc: 0.0415 - ETA: 10s - loss: 13.9582 - acc: 0.0423 - ETA: 9s - loss: 13.9490 - acc: 0.0434  - ETA: 9s - loss: 13.9391 - acc: 0.0453 - ETA: 9s - loss: 13.9081 - acc: 0.0467 - ETA: 8s - loss: 13.8877 - acc: 0.0480 - ETA: 8s - loss: 13.9089 - acc: 0.0477 - ETA: 8s - loss: 13.9008 - acc: 0.0493 - ETA: 7s - loss: 13.8751 - acc: 0.0504 - ETA: 7s - loss: 13.8560 - acc: 0.0511 - ETA: 7s - loss: 13.8597 - acc: 0.0507 - ETA: 6s - loss: 13.8191 - acc: 0.0523 - ETA: 6s - loss: 13.7797 - acc: 0.0529 - ETA: 6s - loss: 13.7571 - acc: 0.0541 - ETA: 6s - loss: 13.7441 - acc: 0.0547 - ETA: 6s - loss: 13.7076 - acc: 0.0570 - ETA: 5s - loss: 13.6589 - acc: 0.0589 - ETA: 5s - loss: 13.6378 - acc: 0.0591 - ETA: 5s - loss: 13.6233 - acc: 0.0597 - ETA: 5s - loss: 13.6160 - acc: 0.0601 - ETA: 5s - loss: 13.6124 - acc: 0.0598 - ETA: 4s - loss: 13.5730 - acc: 0.0610 - ETA: 4s - loss: 13.5444 - acc: 0.0631 - ETA: 4s - loss: 13.5348 - acc: 0.0638 - ETA: 4s - loss: 13.4940 - acc: 0.0656 - ETA: 4s - loss: 13.4656 - acc: 0.0670 - ETA: 3s - loss: 13.4494 - acc: 0.0688 - ETA: 3s - loss: 13.4387 - acc: 0.0694 - ETA: 3s - loss: 13.4316 - acc: 0.0700 - ETA: 3s - loss: 13.4301 - acc: 0.0705 - ETA: 3s - loss: 13.3821 - acc: 0.0722 - ETA: 3s - loss: 13.3536 - acc: 0.0736 - ETA: 3s - loss: 13.3430 - acc: 0.0738 - ETA: 2s - loss: 13.3133 - acc: 0.0753 - ETA: 2s - loss: 13.2933 - acc: 0.0768 - ETA: 2s - loss: 13.2592 - acc: 0.0788 - ETA: 2s - loss: 13.2471 - acc: 0.0798 - ETA: 2s - loss: 13.2386 - acc: 0.0803 - ETA: 2s - loss: 13.2262 - acc: 0.0812 - ETA: 2s - loss: 13.2040 - acc: 0.0822 - ETA: 1s - loss: 13.1649 - acc: 0.0846 - ETA: 1s - loss: 13.1551 - acc: 0.0852 - ETA: 1s - loss: 13.1480 - acc: 0.0858 - ETA: 1s - loss: 13.1394 - acc: 0.0867 - ETA: 1s - loss: 13.1303 - acc: 0.0873 - ETA: 1s - loss: 13.1157 - acc: 0.0878 - ETA: 1s - loss: 13.1287 - acc: 0.0875 - ETA: 1s - loss: 13.1232 - acc: 0.0885 - ETA: 1s - loss: 13.0828 - acc: 0.0902 - ETA: 0s - loss: 13.0515 - acc: 0.0918 - ETA: 0s - loss: 13.0284 - acc: 0.0929 - ETA: 0s - loss: 13.0155 - acc: 0.0938 - ETA: 0s - loss: 12.9963 - acc: 0.0945 - ETA: 0s - loss: 12.9932 - acc: 0.0952 - ETA: 0s - loss: 12.9709 - acc: 0.0970 - ETA: 0s - loss: 12.9563 - acc: 0.0981 - ETA: 0s - loss: 12.9432 - acc: 0.0991 - ETA: 0s - loss: 12.9370 - acc: 0.0994 - ETA: 0s - loss: 12.9109 - acc: 0.1011Epoch 00000: val_loss improved from inf to 11.50167, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 11s - loss: 12.9031 - acc: 0.1010 - val_loss: 11.5017 - val_acc: 0.1880
Epoch 2/20
6600/6680 [============================>.] - ETA: 5s - loss: 9.7305 - acc: 0.3000 - ETA: 5s - loss: 11.4026 - acc: 0.2125 - ETA: 5s - loss: 10.8454 - acc: 0.2625 - ETA: 4s - loss: 10.9029 - acc: 0.2500 - ETA: 4s - loss: 10.7047 - acc: 0.2563 - ETA: 4s - loss: 11.0540 - acc: 0.2375 - ETA: 4s - loss: 11.3234 - acc: 0.2250 - ETA: 4s - loss: 11.1782 - acc: 0.2286 - ETA: 4s - loss: 11.2126 - acc: 0.2250 - ETA: 4s - loss: 11.3074 - acc: 0.2157 - ETA: 4s - loss: 11.3066 - acc: 0.2145 - ETA: 4s - loss: 11.2605 - acc: 0.2146 - ETA: 4s - loss: 11.4012 - acc: 0.2089 - ETA: 4s - loss: 11.3316 - acc: 0.2153 - ETA: 4s - loss: 11.1937 - acc: 0.2245 - ETA: 4s - loss: 11.2581 - acc: 0.2219 - ETA: 4s - loss: 11.1590 - acc: 0.2287 - ETA: 4s - loss: 11.2080 - acc: 0.2262 - ETA: 4s - loss: 11.2728 - acc: 0.2210 - ETA: 3s - loss: 11.2279 - acc: 0.2212 - ETA: 3s - loss: 11.2324 - acc: 0.2214 - ETA: 3s - loss: 11.2675 - acc: 0.2204 - ETA: 3s - loss: 11.2590 - acc: 0.2200 - ETA: 3s - loss: 11.2404 - acc: 0.2230 - ETA: 3s - loss: 11.2381 - acc: 0.2220 - ETA: 3s - loss: 11.2030 - acc: 0.2237 - ETA: 3s - loss: 11.1776 - acc: 0.2252 - ETA: 3s - loss: 11.1653 - acc: 0.2267 - ETA: 3s - loss: 11.1683 - acc: 0.2261 - ETA: 3s - loss: 11.1567 - acc: 0.2274 - ETA: 3s - loss: 11.1544 - acc: 0.2286 - ETA: 3s - loss: 11.1789 - acc: 0.2281 - ETA: 3s - loss: 11.2054 - acc: 0.2272 - ETA: 3s - loss: 11.2235 - acc: 0.2267 - ETA: 3s - loss: 11.1894 - acc: 0.2297 - ETA: 2s - loss: 11.1898 - acc: 0.2307 - ETA: 2s - loss: 11.1976 - acc: 0.2309 - ETA: 2s - loss: 11.1963 - acc: 0.2324 - ETA: 2s - loss: 11.1964 - acc: 0.2322 - ETA: 2s - loss: 11.2052 - acc: 0.2314 - ETA: 2s - loss: 11.1779 - acc: 0.2344 - ETA: 2s - loss: 11.1662 - acc: 0.2351 - ETA: 2s - loss: 11.1394 - acc: 0.2367 - ETA: 2s - loss: 11.1539 - acc: 0.2355 - ETA: 2s - loss: 11.1367 - acc: 0.2370 - ETA: 2s - loss: 11.1264 - acc: 0.2384 - ETA: 2s - loss: 11.1381 - acc: 0.2375 - ETA: 2s - loss: 11.1403 - acc: 0.2364 - ETA: 2s - loss: 11.1335 - acc: 0.2372 - ETA: 2s - loss: 11.1362 - acc: 0.2378 - ETA: 2s - loss: 11.1316 - acc: 0.2388 - ETA: 2s - loss: 11.1545 - acc: 0.2380 - ETA: 1s - loss: 11.1693 - acc: 0.2370 - ETA: 1s - loss: 11.1308 - acc: 0.2387 - ETA: 1s - loss: 11.1003 - acc: 0.2396 - ETA: 1s - loss: 11.1111 - acc: 0.2394 - ETA: 1s - loss: 11.0945 - acc: 0.2395 - ETA: 1s - loss: 11.0993 - acc: 0.2395 - ETA: 1s - loss: 11.0816 - acc: 0.2401 - ETA: 1s - loss: 11.0750 - acc: 0.2401 - ETA: 1s - loss: 11.0888 - acc: 0.2398 - ETA: 1s - loss: 11.0928 - acc: 0.2402 - ETA: 1s - loss: 11.0841 - acc: 0.2410 - ETA: 1s - loss: 11.0862 - acc: 0.2407 - ETA: 1s - loss: 11.0931 - acc: 0.2407 - ETA: 1s - loss: 11.0731 - acc: 0.2424 - ETA: 1s - loss: 11.0640 - acc: 0.2431 - ETA: 1s - loss: 11.0581 - acc: 0.2434 - ETA: 0s - loss: 11.0473 - acc: 0.2442 - ETA: 0s - loss: 11.0550 - acc: 0.2439 - ETA: 0s - loss: 11.0379 - acc: 0.2447 - ETA: 0s - loss: 11.0292 - acc: 0.2455 - ETA: 0s - loss: 11.0181 - acc: 0.2463 - ETA: 0s - loss: 11.0225 - acc: 0.2460 - ETA: 0s - loss: 11.0174 - acc: 0.2461 - ETA: 0s - loss: 11.0111 - acc: 0.2461 - ETA: 0s - loss: 11.0035 - acc: 0.2465 - ETA: 0s - loss: 10.9972 - acc: 0.2467 - ETA: 0s - loss: 10.9853 - acc: 0.2476 - ETA: 0s - loss: 10.9744 - acc: 0.2484 - ETA: 0s - loss: 10.9694 - acc: 0.2491 - ETA: 0s - loss: 10.9653 - acc: 0.2489 - ETA: 0s - loss: 10.9683 - acc: 0.2488 - ETA: 0s - loss: 10.9600 - acc: 0.2492 - ETA: 0s - loss: 10.9761 - acc: 0.2485Epoch 00001: val_loss improved from 11.50167 to 10.87687, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 10.9586 - acc: 0.2496 - val_loss: 10.8769 - val_acc: 0.2311
Epoch 3/20
6640/6680 [============================>.] - ETA: 5s - loss: 10.9619 - acc: 0.3000 - ETA: 5s - loss: 12.2604 - acc: 0.1875 - ETA: 5s - loss: 11.9749 - acc: 0.2143 - ETA: 5s - loss: 11.3742 - acc: 0.2455 - ETA: 5s - loss: 11.1920 - acc: 0.2533 - ETA: 5s - loss: 10.8462 - acc: 0.2737 - ETA: 4s - loss: 10.8757 - acc: 0.2696 - ETA: 4s - loss: 10.8232 - acc: 0.2796 - ETA: 4s - loss: 10.7801 - acc: 0.2839 - ETA: 4s - loss: 10.6900 - acc: 0.2853 - ETA: 4s - loss: 10.7384 - acc: 0.2838 - ETA: 4s - loss: 10.7019 - acc: 0.2866 - ETA: 4s - loss: 10.5970 - acc: 0.2911 - ETA: 4s - loss: 10.5342 - acc: 0.2939 - ETA: 4s - loss: 10.5655 - acc: 0.2915 - ETA: 4s - loss: 10.5649 - acc: 0.2921 - ETA: 4s - loss: 10.5409 - acc: 0.2926 - ETA: 4s - loss: 10.5165 - acc: 0.2945 - ETA: 4s - loss: 10.5369 - acc: 0.2949 - ETA: 4s - loss: 10.5369 - acc: 0.2972 - ETA: 3s - loss: 10.5979 - acc: 0.2928 - ETA: 3s - loss: 10.5999 - acc: 0.2913 - ETA: 3s - loss: 10.5478 - acc: 0.2940 - ETA: 3s - loss: 10.5916 - acc: 0.2920 - ETA: 3s - loss: 10.6009 - acc: 0.2918 - ETA: 3s - loss: 10.6558 - acc: 0.2885 - ETA: 3s - loss: 10.6503 - acc: 0.2885 - ETA: 3s - loss: 10.6813 - acc: 0.2874 - ETA: 3s - loss: 10.6960 - acc: 0.2868 - ETA: 3s - loss: 10.7500 - acc: 0.2832 - ETA: 3s - loss: 10.7112 - acc: 0.2855 - ETA: 3s - loss: 10.7506 - acc: 0.2831 - ETA: 3s - loss: 10.7541 - acc: 0.2828 - ETA: 3s - loss: 10.7734 - acc: 0.2817 - ETA: 3s - loss: 10.7458 - acc: 0.2827 - ETA: 3s - loss: 10.7255 - acc: 0.2832 - ETA: 3s - loss: 10.7301 - acc: 0.2836 - ETA: 2s - loss: 10.7304 - acc: 0.2844 - ETA: 2s - loss: 10.7089 - acc: 0.2862 - ETA: 2s - loss: 10.7053 - acc: 0.2856 - ETA: 2s - loss: 10.7062 - acc: 0.2856 - ETA: 2s - loss: 10.6860 - acc: 0.2857 - ETA: 2s - loss: 10.6125 - acc: 0.2910 - ETA: 2s - loss: 10.6474 - acc: 0.2879 - ETA: 2s - loss: 10.6586 - acc: 0.2873 - ETA: 2s - loss: 10.6637 - acc: 0.2878 - ETA: 2s - loss: 10.6440 - acc: 0.2889 - ETA: 2s - loss: 10.6400 - acc: 0.2897 - ETA: 2s - loss: 10.6325 - acc: 0.2902 - ETA: 2s - loss: 10.6422 - acc: 0.2894 - ETA: 2s - loss: 10.6209 - acc: 0.2906 - ETA: 2s - loss: 10.6190 - acc: 0.2906 - ETA: 2s - loss: 10.6093 - acc: 0.2913 - ETA: 1s - loss: 10.5727 - acc: 0.2931 - ETA: 1s - loss: 10.5753 - acc: 0.2928 - ETA: 1s - loss: 10.5691 - acc: 0.2932 - ETA: 1s - loss: 10.5490 - acc: 0.2940 - ETA: 1s - loss: 10.5580 - acc: 0.2936 - ETA: 1s - loss: 10.5302 - acc: 0.2953 - ETA: 1s - loss: 10.5180 - acc: 0.2963 - ETA: 1s - loss: 10.5059 - acc: 0.2972 - ETA: 1s - loss: 10.5345 - acc: 0.2958 - ETA: 1s - loss: 10.5403 - acc: 0.2956 - ETA: 1s - loss: 10.5364 - acc: 0.2959 - ETA: 1s - loss: 10.5359 - acc: 0.2959 - ETA: 1s - loss: 10.5272 - acc: 0.2966 - ETA: 1s - loss: 10.5314 - acc: 0.2966 - ETA: 1s - loss: 10.5268 - acc: 0.2971 - ETA: 1s - loss: 10.5376 - acc: 0.2963 - ETA: 1s - loss: 10.5536 - acc: 0.2956 - ETA: 1s - loss: 10.5531 - acc: 0.2957 - ETA: 1s - loss: 10.5572 - acc: 0.2955 - ETA: 0s - loss: 10.5519 - acc: 0.2961 - ETA: 0s - loss: 10.5503 - acc: 0.2962 - ETA: 0s - loss: 10.5553 - acc: 0.2961 - ETA: 0s - loss: 10.5616 - acc: 0.2959 - ETA: 0s - loss: 10.5499 - acc: 0.2970 - ETA: 0s - loss: 10.5506 - acc: 0.2971 - ETA: 0s - loss: 10.5400 - acc: 0.2978 - ETA: 0s - loss: 10.5427 - acc: 0.2980 - ETA: 0s - loss: 10.5415 - acc: 0.2982 - ETA: 0s - loss: 10.5497 - acc: 0.2982 - ETA: 0s - loss: 10.5624 - acc: 0.2976 - ETA: 0s - loss: 10.5819 - acc: 0.2968 - ETA: 0s - loss: 10.5803 - acc: 0.2967 - ETA: 0s - loss: 10.5764 - acc: 0.2969 - ETA: 0s - loss: 10.5594 - acc: 0.2981 - ETA: 0s - loss: 10.5502 - acc: 0.2988 - ETA: 0s - loss: 10.5585 - acc: 0.2983Epoch 00002: val_loss improved from 10.87687 to 10.75167, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 10.5630 - acc: 0.2981 - val_loss: 10.7517 - val_acc: 0.2671
Epoch 4/20
6620/6680 [============================>.] - ETA: 5s - loss: 7.2984 - acc: 0.4000 - ETA: 5s - loss: 9.3523 - acc: 0.3700 - ETA: 4s - loss: 9.8570 - acc: 0.3333 - ETA: 4s - loss: 9.8674 - acc: 0.3423 - ETA: 4s - loss: 10.0768 - acc: 0.3324 - ETA: 5s - loss: 9.9982 - acc: 0.3325  - ETA: 5s - loss: 10.1311 - acc: 0.3304 - ETA: 4s - loss: 10.1350 - acc: 0.3222 - ETA: 4s - loss: 10.2350 - acc: 0.3194 - ETA: 4s - loss: 10.3905 - acc: 0.3129 - ETA: 4s - loss: 10.3535 - acc: 0.3167 - ETA: 4s - loss: 10.3088 - acc: 0.3198 - ETA: 4s - loss: 10.2666 - acc: 0.3213 - ETA: 4s - loss: 10.2769 - acc: 0.3206 - ETA: 4s - loss: 10.1939 - acc: 0.3282 - ETA: 4s - loss: 10.1346 - acc: 0.3331 - ETA: 4s - loss: 10.1225 - acc: 0.3357 - ETA: 4s - loss: 10.1183 - acc: 0.3343 - ETA: 4s - loss: 10.1868 - acc: 0.3310 - ETA: 4s - loss: 10.1342 - acc: 0.3333 - ETA: 3s - loss: 10.1432 - acc: 0.3335 - ETA: 3s - loss: 10.1240 - acc: 0.3361 - ETA: 3s - loss: 10.1474 - acc: 0.3345 - ETA: 3s - loss: 10.1038 - acc: 0.3379 - ETA: 3s - loss: 10.1096 - acc: 0.3368 - ETA: 3s - loss: 10.1029 - acc: 0.3379 - ETA: 3s - loss: 10.1215 - acc: 0.3368 - ETA: 3s - loss: 10.1880 - acc: 0.3325 - ETA: 3s - loss: 10.1849 - acc: 0.3330 - ETA: 3s - loss: 10.1630 - acc: 0.3350 - ETA: 3s - loss: 10.1367 - acc: 0.3368 - ETA: 3s - loss: 10.0781 - acc: 0.3409 - ETA: 3s - loss: 10.1057 - acc: 0.3400 - ETA: 3s - loss: 10.1139 - acc: 0.3395 - ETA: 3s - loss: 10.1574 - acc: 0.3361 - ETA: 3s - loss: 10.1065 - acc: 0.3394 - ETA: 2s - loss: 10.1494 - acc: 0.3369 - ETA: 2s - loss: 10.1588 - acc: 0.3362 - ETA: 2s - loss: 10.1346 - acc: 0.3372 - ETA: 2s - loss: 10.1292 - acc: 0.3376 - ETA: 2s - loss: 10.1477 - acc: 0.3362 - ETA: 2s - loss: 10.1450 - acc: 0.3359 - ETA: 2s - loss: 10.1765 - acc: 0.3344 - ETA: 2s - loss: 10.1898 - acc: 0.3338 - ETA: 2s - loss: 10.1977 - acc: 0.3333 - ETA: 2s - loss: 10.2113 - acc: 0.3314 - ETA: 2s - loss: 10.2333 - acc: 0.3299 - ETA: 2s - loss: 10.2068 - acc: 0.3311 - ETA: 2s - loss: 10.2180 - acc: 0.3307 - ETA: 2s - loss: 10.2511 - acc: 0.3291 - ETA: 2s - loss: 10.2316 - acc: 0.3295 - ETA: 2s - loss: 10.2273 - acc: 0.3296 - ETA: 2s - loss: 10.2445 - acc: 0.3288 - ETA: 1s - loss: 10.2755 - acc: 0.3271 - ETA: 1s - loss: 10.2899 - acc: 0.3267 - ETA: 1s - loss: 10.2918 - acc: 0.3268 - ETA: 1s - loss: 10.2985 - acc: 0.3264 - ETA: 1s - loss: 10.2801 - acc: 0.3272 - ETA: 1s - loss: 10.2845 - acc: 0.3266 - ETA: 1s - loss: 10.2925 - acc: 0.3264 - ETA: 1s - loss: 10.3057 - acc: 0.3259 - ETA: 1s - loss: 10.3128 - acc: 0.3256 - ETA: 1s - loss: 10.2885 - acc: 0.3271 - ETA: 1s - loss: 10.2959 - acc: 0.3269 - ETA: 1s - loss: 10.3103 - acc: 0.3260 - ETA: 1s - loss: 10.3292 - acc: 0.3246 - ETA: 1s - loss: 10.2979 - acc: 0.3268 - ETA: 1s - loss: 10.2815 - acc: 0.3275 - ETA: 1s - loss: 10.2957 - acc: 0.3263 - ETA: 1s - loss: 10.2687 - acc: 0.3278 - ETA: 1s - loss: 10.2776 - acc: 0.3276 - ETA: 0s - loss: 10.2730 - acc: 0.3270 - ETA: 0s - loss: 10.2861 - acc: 0.3263 - ETA: 0s - loss: 10.2712 - acc: 0.3271 - ETA: 0s - loss: 10.2609 - acc: 0.3278 - ETA: 0s - loss: 10.2620 - acc: 0.3276 - ETA: 0s - loss: 10.2517 - acc: 0.3282 - ETA: 0s - loss: 10.2695 - acc: 0.3270 - ETA: 0s - loss: 10.2809 - acc: 0.3267 - ETA: 0s - loss: 10.2719 - acc: 0.3271 - ETA: 0s - loss: 10.2650 - acc: 0.3276 - ETA: 0s - loss: 10.2671 - acc: 0.3279 - ETA: 0s - loss: 10.2725 - acc: 0.3275 - ETA: 0s - loss: 10.2667 - acc: 0.3278 - ETA: 0s - loss: 10.2700 - acc: 0.3279 - ETA: 0s - loss: 10.2690 - acc: 0.3282 - ETA: 0s - loss: 10.2704 - acc: 0.3277 - ETA: 0s - loss: 10.2771 - acc: 0.3272Epoch 00003: val_loss improved from 10.75167 to 10.36505, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 10.2728 - acc: 0.3274 - val_loss: 10.3651 - val_acc: 0.3054
Epoch 5/20
6660/6680 [============================>.] - ETA: 5s - loss: 12.0064 - acc: 0.2500 - ETA: 5s - loss: 10.2475 - acc: 0.3100 - ETA: 4s - loss: 10.0142 - acc: 0.3389 - ETA: 4s - loss: 10.1626 - acc: 0.3423 - ETA: 4s - loss: 10.2777 - acc: 0.3344 - ETA: 4s - loss: 10.2103 - acc: 0.3375 - ETA: 4s - loss: 10.1060 - acc: 0.3396 - ETA: 4s - loss: 9.8507 - acc: 0.3536  - ETA: 4s - loss: 9.9456 - acc: 0.3500 - ETA: 4s - loss: 9.9392 - acc: 0.3529 - ETA: 4s - loss: 9.9515 - acc: 0.3538 - ETA: 4s - loss: 10.0896 - acc: 0.3465 - ETA: 4s - loss: 10.2023 - acc: 0.3394 - ETA: 4s - loss: 10.2355 - acc: 0.3382 - ETA: 4s - loss: 10.1235 - acc: 0.3436 - ETA: 4s - loss: 10.1222 - acc: 0.3449 - ETA: 4s - loss: 10.0777 - acc: 0.3476 - ETA: 4s - loss: 10.0686 - acc: 0.3478 - ETA: 4s - loss: 10.1279 - acc: 0.3451 - ETA: 3s - loss: 10.0928 - acc: 0.3473 - ETA: 3s - loss: 10.1005 - acc: 0.3474 - ETA: 3s - loss: 10.0034 - acc: 0.3531 - ETA: 3s - loss: 9.9646 - acc: 0.3553  - ETA: 3s - loss: 9.9889 - acc: 0.3545 - ETA: 3s - loss: 10.0050 - acc: 0.3538 - ETA: 3s - loss: 10.0277 - acc: 0.3521 - ETA: 3s - loss: 10.0466 - acc: 0.3510 - ETA: 3s - loss: 10.0862 - acc: 0.3495 - ETA: 3s - loss: 10.0380 - acc: 0.3523 - ETA: 3s - loss: 9.9841 - acc: 0.3544  - ETA: 3s - loss: 9.9210 - acc: 0.3581 - ETA: 3s - loss: 9.9214 - acc: 0.3583 - ETA: 3s - loss: 9.9240 - acc: 0.3580 - ETA: 3s - loss: 9.9201 - acc: 0.3585 - ETA: 3s - loss: 9.9218 - acc: 0.3586 - ETA: 3s - loss: 9.8995 - acc: 0.3599 - ETA: 2s - loss: 9.9150 - acc: 0.3582 - ETA: 2s - loss: 9.8931 - acc: 0.3601 - ETA: 2s - loss: 9.9197 - acc: 0.3584 - ETA: 2s - loss: 9.9374 - acc: 0.3576 - ETA: 2s - loss: 9.9315 - acc: 0.3568 - ETA: 2s - loss: 9.9325 - acc: 0.3569 - ETA: 2s - loss: 9.9822 - acc: 0.3537 - ETA: 2s - loss: 9.9788 - acc: 0.3542 - ETA: 2s - loss: 10.0251 - acc: 0.3512 - ETA: 2s - loss: 10.0480 - acc: 0.3500 - ETA: 2s - loss: 10.0312 - acc: 0.3514 - ETA: 2s - loss: 10.0204 - acc: 0.3525 - ETA: 2s - loss: 10.0354 - acc: 0.3513 - ETA: 2s - loss: 10.0194 - acc: 0.3516 - ETA: 2s - loss: 9.9953 - acc: 0.3526  - ETA: 2s - loss: 9.9862 - acc: 0.3533 - ETA: 1s - loss: 9.9798 - acc: 0.3537 - ETA: 1s - loss: 9.9792 - acc: 0.3539 - ETA: 1s - loss: 10.0051 - acc: 0.3521 - ETA: 1s - loss: 9.9881 - acc: 0.3535  - ETA: 1s - loss: 9.9960 - acc: 0.3530 - ETA: 1s - loss: 10.0020 - acc: 0.3527 - ETA: 1s - loss: 10.0111 - acc: 0.3520 - ETA: 1s - loss: 10.0067 - acc: 0.3522 - ETA: 1s - loss: 10.0173 - acc: 0.3515 - ETA: 1s - loss: 10.0429 - acc: 0.3498 - ETA: 1s - loss: 10.0378 - acc: 0.3500 - ETA: 1s - loss: 10.0229 - acc: 0.3510 - ETA: 1s - loss: 10.0337 - acc: 0.3508 - ETA: 1s - loss: 10.0418 - acc: 0.3504 - ETA: 1s - loss: 10.0393 - acc: 0.3510 - ETA: 1s - loss: 10.0506 - acc: 0.3502 - ETA: 1s - loss: 10.0451 - acc: 0.3506 - ETA: 0s - loss: 10.0238 - acc: 0.3511 - ETA: 0s - loss: 10.0254 - acc: 0.3511 - ETA: 0s - loss: 10.0347 - acc: 0.3505 - ETA: 0s - loss: 10.0254 - acc: 0.3511 - ETA: 0s - loss: 10.0292 - acc: 0.3509 - ETA: 0s - loss: 10.0173 - acc: 0.3517 - ETA: 0s - loss: 9.9944 - acc: 0.3532  - ETA: 0s - loss: 10.0224 - acc: 0.3517 - ETA: 0s - loss: 10.0281 - acc: 0.3513 - ETA: 0s - loss: 10.0290 - acc: 0.3510 - ETA: 0s - loss: 10.0376 - acc: 0.3503 - ETA: 0s - loss: 10.0390 - acc: 0.3500 - ETA: 0s - loss: 10.0559 - acc: 0.3491 - ETA: 0s - loss: 10.0653 - acc: 0.3484 - ETA: 0s - loss: 10.0565 - acc: 0.3486 - ETA: 0s - loss: 10.0421 - acc: 0.3498 - ETA: 0s - loss: 10.0426 - acc: 0.3502Epoch 00004: val_loss improved from 10.36505 to 10.28750, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 10.0488 - acc: 0.3499 - val_loss: 10.2875 - val_acc: 0.3054
Epoch 6/20
6640/6680 [============================>.] - ETA: 5s - loss: 10.5118 - acc: 0.3500 - ETA: 4s - loss: 10.7505 - acc: 0.3200 - ETA: 4s - loss: 10.9094 - acc: 0.3111 - ETA: 4s - loss: 10.9045 - acc: 0.3115 - ETA: 4s - loss: 10.6242 - acc: 0.3235 - ETA: 4s - loss: 10.4881 - acc: 0.3310 - ETA: 4s - loss: 10.3366 - acc: 0.3400 - ETA: 4s - loss: 10.1754 - acc: 0.3466 - ETA: 4s - loss: 10.0366 - acc: 0.3515 - ETA: 4s - loss: 9.9900 - acc: 0.3541  - ETA: 4s - loss: 10.0680 - acc: 0.3488 - ETA: 4s - loss: 9.9296 - acc: 0.3589  - ETA: 4s - loss: 9.8503 - acc: 0.3643 - ETA: 4s - loss: 9.8556 - acc: 0.3654 - ETA: 4s - loss: 9.7816 - acc: 0.3696 - ETA: 4s - loss: 9.7896 - acc: 0.3700 - ETA: 4s - loss: 9.7730 - acc: 0.3711 - ETA: 3s - loss: 9.7251 - acc: 0.3743 - ETA: 3s - loss: 9.7128 - acc: 0.3754 - ETA: 3s - loss: 9.7339 - acc: 0.3747 - ETA: 3s - loss: 9.7742 - acc: 0.3728 - ETA: 3s - loss: 9.7906 - acc: 0.3723 - ETA: 3s - loss: 9.8440 - acc: 0.3695 - ETA: 3s - loss: 9.8968 - acc: 0.3665 - ETA: 3s - loss: 9.8493 - acc: 0.3695 - ETA: 3s - loss: 9.8827 - acc: 0.3657 - ETA: 3s - loss: 9.8979 - acc: 0.3641 - ETA: 3s - loss: 9.9299 - acc: 0.3626 - ETA: 3s - loss: 9.9799 - acc: 0.3595 - ETA: 3s - loss: 9.9932 - acc: 0.3591 - ETA: 3s - loss: 9.9700 - acc: 0.3609 - ETA: 3s - loss: 9.8960 - acc: 0.3654 - ETA: 3s - loss: 9.9141 - acc: 0.3638 - ETA: 3s - loss: 9.9270 - acc: 0.3630 - ETA: 3s - loss: 9.9147 - acc: 0.3634 - ETA: 2s - loss: 9.9131 - acc: 0.3628 - ETA: 2s - loss: 9.9348 - acc: 0.3614 - ETA: 2s - loss: 9.9011 - acc: 0.3628 - ETA: 2s - loss: 9.8992 - acc: 0.3632 - ETA: 2s - loss: 9.9300 - acc: 0.3615 - ETA: 2s - loss: 9.9693 - acc: 0.3590 - ETA: 2s - loss: 9.9805 - acc: 0.3572 - ETA: 2s - loss: 9.9707 - acc: 0.3579 - ETA: 2s - loss: 9.9729 - acc: 0.3571 - ETA: 2s - loss: 9.9771 - acc: 0.3570 - ETA: 2s - loss: 9.9620 - acc: 0.3577 - ETA: 2s - loss: 9.9599 - acc: 0.3575 - ETA: 2s - loss: 9.9174 - acc: 0.3595 - ETA: 2s - loss: 9.8927 - acc: 0.3612 - ETA: 2s - loss: 9.8943 - acc: 0.3612 - ETA: 2s - loss: 9.8915 - acc: 0.3617 - ETA: 2s - loss: 9.8978 - acc: 0.3613 - ETA: 1s - loss: 9.9044 - acc: 0.3610 - ETA: 1s - loss: 9.9176 - acc: 0.3603 - ETA: 1s - loss: 9.8937 - acc: 0.3620 - ETA: 1s - loss: 9.8705 - acc: 0.3634 - ETA: 1s - loss: 9.8578 - acc: 0.3641 - ETA: 1s - loss: 9.8428 - acc: 0.3647 - ETA: 1s - loss: 9.8421 - acc: 0.3649 - ETA: 1s - loss: 9.8548 - acc: 0.3643 - ETA: 1s - loss: 9.8594 - acc: 0.3638 - ETA: 1s - loss: 9.8412 - acc: 0.3649 - ETA: 1s - loss: 9.8406 - acc: 0.3644 - ETA: 1s - loss: 9.8269 - acc: 0.3654 - ETA: 1s - loss: 9.8505 - acc: 0.3635 - ETA: 1s - loss: 9.8646 - acc: 0.3625 - ETA: 1s - loss: 9.8614 - acc: 0.3627 - ETA: 1s - loss: 9.8475 - acc: 0.3631 - ETA: 1s - loss: 9.8526 - acc: 0.3627 - ETA: 0s - loss: 9.8726 - acc: 0.3614 - ETA: 0s - loss: 9.8687 - acc: 0.3615 - ETA: 0s - loss: 9.8549 - acc: 0.3621 - ETA: 0s - loss: 9.8605 - acc: 0.3614 - ETA: 0s - loss: 9.8465 - acc: 0.3623 - ETA: 0s - loss: 9.8444 - acc: 0.3628 - ETA: 0s - loss: 9.8192 - acc: 0.3644 - ETA: 0s - loss: 9.8261 - acc: 0.3637 - ETA: 0s - loss: 9.8311 - acc: 0.3633 - ETA: 0s - loss: 9.8167 - acc: 0.3638 - ETA: 0s - loss: 9.8314 - acc: 0.3630 - ETA: 0s - loss: 9.8230 - acc: 0.3635 - ETA: 0s - loss: 9.8264 - acc: 0.3636 - ETA: 0s - loss: 9.8421 - acc: 0.3625 - ETA: 0s - loss: 9.8334 - acc: 0.3631 - ETA: 0s - loss: 9.8216 - acc: 0.3642 - ETA: 0s - loss: 9.8225 - acc: 0.3643Epoch 00005: val_loss improved from 10.28750 to 9.98177, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 9.8144 - acc: 0.3650 - val_loss: 9.9818 - val_acc: 0.3234
Epoch 7/20
6660/6680 [============================>.] - ETA: 5s - loss: 9.6711 - acc: 0.4000 - ETA: 5s - loss: 9.0513 - acc: 0.4300 - ETA: 4s - loss: 9.3138 - acc: 0.4111 - ETA: 4s - loss: 9.7033 - acc: 0.3885 - ETA: 4s - loss: 9.4624 - acc: 0.4059 - ETA: 4s - loss: 9.8791 - acc: 0.3786 - ETA: 4s - loss: 9.7210 - acc: 0.3860 - ETA: 4s - loss: 9.7188 - acc: 0.3862 - ETA: 4s - loss: 9.7828 - acc: 0.3818 - ETA: 4s - loss: 9.7317 - acc: 0.3838 - ETA: 4s - loss: 9.7455 - acc: 0.3841 - ETA: 4s - loss: 9.7577 - acc: 0.3833 - ETA: 4s - loss: 9.7636 - acc: 0.3816 - ETA: 4s - loss: 9.7705 - acc: 0.3802 - ETA: 4s - loss: 9.7893 - acc: 0.3786 - ETA: 4s - loss: 9.6899 - acc: 0.3847 - ETA: 4s - loss: 9.6792 - acc: 0.3839 - ETA: 4s - loss: 9.7062 - acc: 0.3803 - ETA: 4s - loss: 9.7473 - acc: 0.3768 - ETA: 3s - loss: 9.6915 - acc: 0.3795 - ETA: 3s - loss: 9.7178 - acc: 0.3779 - ETA: 3s - loss: 9.7136 - acc: 0.3784 - ETA: 3s - loss: 9.7135 - acc: 0.3776 - ETA: 3s - loss: 9.6724 - acc: 0.3792 - ETA: 3s - loss: 9.6688 - acc: 0.3790 - ETA: 3s - loss: 9.6766 - acc: 0.3768 - ETA: 3s - loss: 9.7190 - acc: 0.3748 - ETA: 3s - loss: 9.7065 - acc: 0.3733 - ETA: 3s - loss: 9.6825 - acc: 0.3725 - ETA: 3s - loss: 9.7028 - acc: 0.3712 - ETA: 3s - loss: 9.6839 - acc: 0.3722 - ETA: 3s - loss: 9.6775 - acc: 0.3736 - ETA: 3s - loss: 9.6530 - acc: 0.3756 - ETA: 3s - loss: 9.6458 - acc: 0.3764 - ETA: 3s - loss: 9.6429 - acc: 0.3767 - ETA: 2s - loss: 9.6258 - acc: 0.3774 - ETA: 2s - loss: 9.6138 - acc: 0.3777 - ETA: 2s - loss: 9.6291 - acc: 0.3769 - ETA: 2s - loss: 9.6076 - acc: 0.3775 - ETA: 2s - loss: 9.5990 - acc: 0.3778 - ETA: 2s - loss: 9.5986 - acc: 0.3774 - ETA: 2s - loss: 9.6351 - acc: 0.3752 - ETA: 2s - loss: 9.6337 - acc: 0.3753 - ETA: 2s - loss: 9.6474 - acc: 0.3750 - ETA: 2s - loss: 9.6333 - acc: 0.3759 - ETA: 2s - loss: 9.6076 - acc: 0.3778 - ETA: 2s - loss: 9.5770 - acc: 0.3794 - ETA: 2s - loss: 9.5718 - acc: 0.3793 - ETA: 2s - loss: 9.5578 - acc: 0.3806 - ETA: 2s - loss: 9.5547 - acc: 0.3807 - ETA: 2s - loss: 9.5433 - acc: 0.3814 - ETA: 2s - loss: 9.5253 - acc: 0.3817 - ETA: 2s - loss: 9.5378 - acc: 0.3812 - ETA: 1s - loss: 9.5691 - acc: 0.3793 - ETA: 1s - loss: 9.5526 - acc: 0.3803 - ETA: 1s - loss: 9.5467 - acc: 0.3810 - ETA: 1s - loss: 9.5455 - acc: 0.3808 - ETA: 1s - loss: 9.5277 - acc: 0.3818 - ETA: 1s - loss: 9.5303 - acc: 0.3818 - ETA: 1s - loss: 9.5289 - acc: 0.3821 - ETA: 1s - loss: 9.5344 - acc: 0.3821 - ETA: 1s - loss: 9.5334 - acc: 0.3826 - ETA: 1s - loss: 9.5379 - acc: 0.3823 - ETA: 1s - loss: 9.5595 - acc: 0.3809 - ETA: 1s - loss: 9.5547 - acc: 0.3816 - ETA: 1s - loss: 9.5681 - acc: 0.3809 - ETA: 1s - loss: 9.5870 - acc: 0.3800 - ETA: 1s - loss: 9.5830 - acc: 0.3805 - ETA: 1s - loss: 9.5669 - acc: 0.3818 - ETA: 1s - loss: 9.5449 - acc: 0.3832 - ETA: 1s - loss: 9.5155 - acc: 0.3846 - ETA: 0s - loss: 9.5122 - acc: 0.3848 - ETA: 0s - loss: 9.5013 - acc: 0.3855 - ETA: 0s - loss: 9.4870 - acc: 0.3865 - ETA: 0s - loss: 9.4822 - acc: 0.3867 - ETA: 0s - loss: 9.4754 - acc: 0.3872 - ETA: 0s - loss: 9.4893 - acc: 0.3865 - ETA: 0s - loss: 9.4923 - acc: 0.3863 - ETA: 0s - loss: 9.5046 - acc: 0.3850 - ETA: 0s - loss: 9.4966 - acc: 0.3852 - ETA: 0s - loss: 9.4941 - acc: 0.3854 - ETA: 0s - loss: 9.5001 - acc: 0.3851 - ETA: 0s - loss: 9.5011 - acc: 0.3845 - ETA: 0s - loss: 9.5234 - acc: 0.3834 - ETA: 0s - loss: 9.5246 - acc: 0.3832 - ETA: 0s - loss: 9.5267 - acc: 0.3826 - ETA: 0s - loss: 9.5203 - acc: 0.3827 - ETA: 0s - loss: 9.4888 - acc: 0.3842Epoch 00006: val_loss improved from 9.98177 to 9.79691, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 9.4894 - acc: 0.3843 - val_loss: 9.7969 - val_acc: 0.3293
Epoch 8/20
6660/6680 [============================>.] - ETA: 5s - loss: 9.7071 - acc: 0.4000 - ETA: 5s - loss: 8.5327 - acc: 0.4375 - ETA: 5s - loss: 8.9941 - acc: 0.4062 - ETA: 4s - loss: 8.8893 - acc: 0.4250 - ETA: 4s - loss: 9.2332 - acc: 0.4031 - ETA: 4s - loss: 9.2877 - acc: 0.4025 - ETA: 4s - loss: 8.9349 - acc: 0.4250 - ETA: 4s - loss: 8.9470 - acc: 0.4214 - ETA: 4s - loss: 8.9377 - acc: 0.4250 - ETA: 4s - loss: 8.9309 - acc: 0.4236 - ETA: 4s - loss: 8.9968 - acc: 0.4213 - ETA: 4s - loss: 9.1514 - acc: 0.4114 - ETA: 4s - loss: 9.1375 - acc: 0.4094 - ETA: 4s - loss: 9.0558 - acc: 0.4163 - ETA: 4s - loss: 9.1104 - acc: 0.4116 - ETA: 4s - loss: 9.0976 - acc: 0.4125 - ETA: 4s - loss: 9.0792 - acc: 0.4143 - ETA: 4s - loss: 9.1677 - acc: 0.4098 - ETA: 4s - loss: 9.1092 - acc: 0.4138 - ETA: 4s - loss: 9.1795 - acc: 0.4097 - ETA: 4s - loss: 9.2328 - acc: 0.4060 - ETA: 4s - loss: 9.2294 - acc: 0.4071 - ETA: 3s - loss: 9.1917 - acc: 0.4093 - ETA: 3s - loss: 9.1561 - acc: 0.4113 - ETA: 3s - loss: 9.1289 - acc: 0.4132 - ETA: 3s - loss: 9.0754 - acc: 0.4172 - ETA: 3s - loss: 9.0481 - acc: 0.4177 - ETA: 3s - loss: 9.0737 - acc: 0.4161 - ETA: 3s - loss: 9.0743 - acc: 0.4167 - ETA: 3s - loss: 9.0338 - acc: 0.4172 - ETA: 3s - loss: 8.9928 - acc: 0.4200 - ETA: 3s - loss: 8.9702 - acc: 0.4208 - ETA: 3s - loss: 8.9906 - acc: 0.4194 - ETA: 3s - loss: 9.0152 - acc: 0.4171 - ETA: 3s - loss: 8.9694 - acc: 0.4208 - ETA: 3s - loss: 9.0124 - acc: 0.4176 - ETA: 3s - loss: 9.0421 - acc: 0.4163 - ETA: 3s - loss: 9.0329 - acc: 0.4173 - ETA: 3s - loss: 9.0848 - acc: 0.4134 - ETA: 3s - loss: 9.1198 - acc: 0.4112 - ETA: 3s - loss: 9.1759 - acc: 0.4074 - ETA: 3s - loss: 9.1859 - acc: 0.4068 - ETA: 2s - loss: 9.1589 - acc: 0.4077 - ETA: 2s - loss: 9.1570 - acc: 0.4081 - ETA: 2s - loss: 9.1679 - acc: 0.4076 - ETA: 2s - loss: 9.1860 - acc: 0.4065 - ETA: 2s - loss: 9.1875 - acc: 0.4060 - ETA: 2s - loss: 9.1926 - acc: 0.4059 - ETA: 2s - loss: 9.1599 - acc: 0.4075 - ETA: 2s - loss: 9.1127 - acc: 0.4110 - ETA: 2s - loss: 9.1386 - acc: 0.4099 - ETA: 2s - loss: 9.1421 - acc: 0.4099 - ETA: 2s - loss: 9.1376 - acc: 0.4103 - ETA: 2s - loss: 9.1257 - acc: 0.4108 - ETA: 2s - loss: 9.1452 - acc: 0.4098 - ETA: 2s - loss: 9.1836 - acc: 0.4079 - ETA: 2s - loss: 9.1751 - acc: 0.4083 - ETA: 1s - loss: 9.1581 - acc: 0.4090 - ETA: 1s - loss: 9.1698 - acc: 0.4084 - ETA: 1s - loss: 9.1794 - acc: 0.4076 - ETA: 1s - loss: 9.1842 - acc: 0.4072 - ETA: 1s - loss: 9.1867 - acc: 0.4071 - ETA: 1s - loss: 9.1767 - acc: 0.4079 - ETA: 1s - loss: 9.1735 - acc: 0.4082 - ETA: 1s - loss: 9.1727 - acc: 0.4085 - ETA: 1s - loss: 9.1648 - acc: 0.4090 - ETA: 1s - loss: 9.1900 - acc: 0.4076 - ETA: 1s - loss: 9.1989 - acc: 0.4071 - ETA: 1s - loss: 9.1935 - acc: 0.4072 - ETA: 1s - loss: 9.2251 - acc: 0.4053 - ETA: 1s - loss: 9.2203 - acc: 0.4056 - ETA: 1s - loss: 9.2322 - acc: 0.4048 - ETA: 1s - loss: 9.2426 - acc: 0.4043 - ETA: 1s - loss: 9.2410 - acc: 0.4044 - ETA: 0s - loss: 9.2272 - acc: 0.4053 - ETA: 0s - loss: 9.2432 - acc: 0.4045 - ETA: 0s - loss: 9.2586 - acc: 0.4034 - ETA: 0s - loss: 9.2701 - acc: 0.4028 - ETA: 0s - loss: 9.2849 - acc: 0.4017 - ETA: 0s - loss: 9.2824 - acc: 0.4022 - ETA: 0s - loss: 9.2787 - acc: 0.4023 - ETA: 0s - loss: 9.2770 - acc: 0.4025 - ETA: 0s - loss: 9.2636 - acc: 0.4033 - ETA: 0s - loss: 9.2698 - acc: 0.4031 - ETA: 0s - loss: 9.2765 - acc: 0.4024 - ETA: 0s - loss: 9.2744 - acc: 0.4027 - ETA: 0s - loss: 9.2947 - acc: 0.4014 - ETA: 0s - loss: 9.3106 - acc: 0.4006 - ETA: 0s - loss: 9.3113 - acc: 0.4002 - ETA: 0s - loss: 9.3157 - acc: 0.4000Epoch 00007: val_loss improved from 9.79691 to 9.73454, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 9.3192 - acc: 0.3999 - val_loss: 9.7345 - val_acc: 0.3365
Epoch 9/20
6620/6680 [============================>.] - ETA: 5s - loss: 7.7527 - acc: 0.5000 - ETA: 4s - loss: 9.1372 - acc: 0.4200 - ETA: 4s - loss: 9.2438 - acc: 0.4056 - ETA: 4s - loss: 9.0040 - acc: 0.4269 - ETA: 4s - loss: 9.2582 - acc: 0.4147 - ETA: 4s - loss: 9.5292 - acc: 0.4000 - ETA: 4s - loss: 9.5154 - acc: 0.4021 - ETA: 4s - loss: 9.4432 - acc: 0.4074 - ETA: 4s - loss: 9.1798 - acc: 0.4217 - ETA: 4s - loss: 9.1516 - acc: 0.4242 - ETA: 4s - loss: 9.1328 - acc: 0.4250 - ETA: 4s - loss: 9.1202 - acc: 0.4237 - ETA: 4s - loss: 9.1598 - acc: 0.4205 - ETA: 4s - loss: 9.1735 - acc: 0.4198 - ETA: 4s - loss: 9.2284 - acc: 0.4173 - ETA: 4s - loss: 9.3635 - acc: 0.4089 - ETA: 4s - loss: 9.3491 - acc: 0.4092 - ETA: 4s - loss: 9.3024 - acc: 0.4117 - ETA: 4s - loss: 9.3351 - acc: 0.4097 - ETA: 4s - loss: 9.3047 - acc: 0.4114 - ETA: 4s - loss: 9.2758 - acc: 0.4137 - ETA: 4s - loss: 9.2809 - acc: 0.4138 - ETA: 4s - loss: 9.2863 - acc: 0.4133 - ETA: 3s - loss: 9.2711 - acc: 0.4146 - ETA: 3s - loss: 9.3045 - acc: 0.4129 - ETA: 3s - loss: 9.3449 - acc: 0.4102 - ETA: 3s - loss: 9.3309 - acc: 0.4110 - ETA: 3s - loss: 9.2138 - acc: 0.4179 - ETA: 3s - loss: 9.1918 - acc: 0.4197 - ETA: 3s - loss: 9.1834 - acc: 0.4196 - ETA: 3s - loss: 9.1760 - acc: 0.4200 - ETA: 3s - loss: 9.1313 - acc: 0.4225 - ETA: 3s - loss: 9.1792 - acc: 0.4199 - ETA: 3s - loss: 9.2010 - acc: 0.4179 - ETA: 3s - loss: 9.2039 - acc: 0.4178 - ETA: 3s - loss: 9.2046 - acc: 0.4173 - ETA: 3s - loss: 9.2010 - acc: 0.4176 - ETA: 3s - loss: 9.2474 - acc: 0.4148 - ETA: 3s - loss: 9.2225 - acc: 0.4165 - ETA: 3s - loss: 9.2111 - acc: 0.4169 - ETA: 3s - loss: 9.2058 - acc: 0.4169 - ETA: 3s - loss: 9.2408 - acc: 0.4151 - ETA: 2s - loss: 9.2045 - acc: 0.4173 - ETA: 2s - loss: 9.2322 - acc: 0.4153 - ETA: 2s - loss: 9.1978 - acc: 0.4174 - ETA: 2s - loss: 9.2173 - acc: 0.4158 - ETA: 2s - loss: 9.2131 - acc: 0.4162 - ETA: 2s - loss: 9.2063 - acc: 0.4159 - ETA: 2s - loss: 9.2038 - acc: 0.4158 - ETA: 2s - loss: 9.1879 - acc: 0.4166 - ETA: 2s - loss: 9.1687 - acc: 0.4173 - ETA: 2s - loss: 9.1888 - acc: 0.4161 - ETA: 2s - loss: 9.1811 - acc: 0.4160 - ETA: 2s - loss: 9.1749 - acc: 0.4168 - ETA: 2s - loss: 9.1602 - acc: 0.4177 - ETA: 2s - loss: 9.1579 - acc: 0.4178 - ETA: 2s - loss: 9.1724 - acc: 0.4170 - ETA: 2s - loss: 9.1575 - acc: 0.4179 - ETA: 1s - loss: 9.1747 - acc: 0.4166 - ETA: 1s - loss: 9.1758 - acc: 0.4167 - ETA: 1s - loss: 9.1785 - acc: 0.4167 - ETA: 1s - loss: 9.1825 - acc: 0.4164 - ETA: 1s - loss: 9.1884 - acc: 0.4156 - ETA: 1s - loss: 9.2055 - acc: 0.4148 - ETA: 1s - loss: 9.2213 - acc: 0.4139 - ETA: 1s - loss: 9.2147 - acc: 0.4139 - ETA: 1s - loss: 9.2169 - acc: 0.4136 - ETA: 1s - loss: 9.2047 - acc: 0.4146 - ETA: 1s - loss: 9.2168 - acc: 0.4136 - ETA: 1s - loss: 9.2320 - acc: 0.4128 - ETA: 1s - loss: 9.2265 - acc: 0.4132 - ETA: 1s - loss: 9.2218 - acc: 0.4135 - ETA: 1s - loss: 9.2107 - acc: 0.4143 - ETA: 1s - loss: 9.2162 - acc: 0.4141 - ETA: 0s - loss: 9.2236 - acc: 0.4135 - ETA: 0s - loss: 9.2245 - acc: 0.4135 - ETA: 0s - loss: 9.2307 - acc: 0.4133 - ETA: 0s - loss: 9.2350 - acc: 0.4131 - ETA: 0s - loss: 9.2407 - acc: 0.4128 - ETA: 0s - loss: 9.2303 - acc: 0.4136 - ETA: 0s - loss: 9.2197 - acc: 0.4141 - ETA: 0s - loss: 9.2088 - acc: 0.4146 - ETA: 0s - loss: 9.2008 - acc: 0.4151 - ETA: 0s - loss: 9.2032 - acc: 0.4150 - ETA: 0s - loss: 9.2147 - acc: 0.4145 - ETA: 0s - loss: 9.2210 - acc: 0.4141 - ETA: 0s - loss: 9.2257 - acc: 0.4136 - ETA: 0s - loss: 9.2295 - acc: 0.4135 - ETA: 0s - loss: 9.2301 - acc: 0.4136 - ETA: 0s - loss: 9.2439 - acc: 0.4124Epoch 00008: val_loss improved from 9.73454 to 9.73435, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 9.2526 - acc: 0.4120 - val_loss: 9.7344 - val_acc: 0.3449
Epoch 10/20
6660/6680 [============================>.] - ETA: 4s - loss: 10.4770 - acc: 0.3500 - ETA: 4s - loss: 9.2315 - acc: 0.4200  - ETA: 4s - loss: 9.7127 - acc: 0.3833 - ETA: 4s - loss: 9.1045 - acc: 0.4154 - ETA: 4s - loss: 8.9085 - acc: 0.4294 - ETA: 4s - loss: 9.1330 - acc: 0.4167 - ETA: 4s - loss: 9.5235 - acc: 0.3940 - ETA: 4s - loss: 9.5718 - acc: 0.3931 - ETA: 4s - loss: 9.3643 - acc: 0.4076 - ETA: 4s - loss: 9.3413 - acc: 0.4095 - ETA: 4s - loss: 9.3541 - acc: 0.4098 - ETA: 4s - loss: 9.3499 - acc: 0.4100 - ETA: 4s - loss: 9.3928 - acc: 0.4082 - ETA: 4s - loss: 9.2828 - acc: 0.4151 - ETA: 4s - loss: 9.2687 - acc: 0.4158 - ETA: 4s - loss: 9.2558 - acc: 0.4172 - ETA: 4s - loss: 9.2944 - acc: 0.4154 - ETA: 3s - loss: 9.3468 - acc: 0.4123 - ETA: 3s - loss: 9.3139 - acc: 0.4123 - ETA: 3s - loss: 9.3338 - acc: 0.4110 - ETA: 3s - loss: 9.3605 - acc: 0.4099 - ETA: 3s - loss: 9.3505 - acc: 0.4106 - ETA: 3s - loss: 9.3434 - acc: 0.4114 - ETA: 3s - loss: 9.3772 - acc: 0.4082 - ETA: 3s - loss: 9.3630 - acc: 0.4085 - ETA: 3s - loss: 9.3179 - acc: 0.4107 - ETA: 3s - loss: 9.3655 - acc: 0.4074 - ETA: 3s - loss: 9.3625 - acc: 0.4075 - ETA: 3s - loss: 9.3608 - acc: 0.4077 - ETA: 3s - loss: 9.3872 - acc: 0.4057 - ETA: 3s - loss: 9.3712 - acc: 0.4068 - ETA: 3s - loss: 9.3638 - acc: 0.4070 - ETA: 3s - loss: 9.3353 - acc: 0.4091 - ETA: 3s - loss: 9.3453 - acc: 0.4069 - ETA: 3s - loss: 9.3452 - acc: 0.4067 - ETA: 2s - loss: 9.3581 - acc: 0.4062 - ETA: 2s - loss: 9.3636 - acc: 0.4056 - ETA: 2s - loss: 9.3901 - acc: 0.4041 - ETA: 2s - loss: 9.3721 - acc: 0.4053 - ETA: 2s - loss: 9.3537 - acc: 0.4068 - ETA: 2s - loss: 9.3445 - acc: 0.4076 - ETA: 2s - loss: 9.3496 - acc: 0.4075 - ETA: 2s - loss: 9.3085 - acc: 0.4100 - ETA: 2s - loss: 9.2987 - acc: 0.4107 - ETA: 2s - loss: 9.3081 - acc: 0.4101 - ETA: 2s - loss: 9.3026 - acc: 0.4105 - ETA: 2s - loss: 9.2934 - acc: 0.4110 - ETA: 2s - loss: 9.3026 - acc: 0.4105 - ETA: 2s - loss: 9.2895 - acc: 0.4114 - ETA: 2s - loss: 9.2997 - acc: 0.4109 - ETA: 2s - loss: 9.2972 - acc: 0.4113 - ETA: 2s - loss: 9.2948 - acc: 0.4116 - ETA: 2s - loss: 9.2765 - acc: 0.4129 - ETA: 1s - loss: 9.2824 - acc: 0.4127 - ETA: 1s - loss: 9.2969 - acc: 0.4118 - ETA: 1s - loss: 9.2873 - acc: 0.4124 - ETA: 1s - loss: 9.2793 - acc: 0.4127 - ETA: 1s - loss: 9.2734 - acc: 0.4127 - ETA: 1s - loss: 9.2857 - acc: 0.4120 - ETA: 1s - loss: 9.3078 - acc: 0.4107 - ETA: 1s - loss: 9.3247 - acc: 0.4095 - ETA: 1s - loss: 9.3292 - acc: 0.4089 - ETA: 1s - loss: 9.3137 - acc: 0.4100 - ETA: 1s - loss: 9.3071 - acc: 0.4105 - ETA: 1s - loss: 9.2952 - acc: 0.4109 - ETA: 1s - loss: 9.2801 - acc: 0.4115 - ETA: 1s - loss: 9.2930 - acc: 0.4110 - ETA: 1s - loss: 9.2989 - acc: 0.4108 - ETA: 1s - loss: 9.3016 - acc: 0.4108 - ETA: 1s - loss: 9.3004 - acc: 0.4108 - ETA: 1s - loss: 9.2942 - acc: 0.4112 - ETA: 0s - loss: 9.3028 - acc: 0.4109 - ETA: 0s - loss: 9.2965 - acc: 0.4115 - ETA: 0s - loss: 9.2884 - acc: 0.4118 - ETA: 0s - loss: 9.2968 - acc: 0.4111 - ETA: 0s - loss: 9.2965 - acc: 0.4113 - ETA: 0s - loss: 9.2816 - acc: 0.4120 - ETA: 0s - loss: 9.2751 - acc: 0.4122 - ETA: 0s - loss: 9.2768 - acc: 0.4121 - ETA: 0s - loss: 9.2821 - acc: 0.4120 - ETA: 0s - loss: 9.2670 - acc: 0.4128 - ETA: 0s - loss: 9.2608 - acc: 0.4129 - ETA: 0s - loss: 9.2460 - acc: 0.4139 - ETA: 0s - loss: 9.2483 - acc: 0.4138 - ETA: 0s - loss: 9.2489 - acc: 0.4138 - ETA: 0s - loss: 9.2477 - acc: 0.4138 - ETA: 0s - loss: 9.2447 - acc: 0.4140 - ETA: 0s - loss: 9.2333 - acc: 0.4144 - ETA: 0s - loss: 9.2133 - acc: 0.4155Epoch 00009: val_loss improved from 9.73435 to 9.58462, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 9.2123 - acc: 0.4156 - val_loss: 9.5846 - val_acc: 0.3413
Epoch 11/20
6600/6680 [============================>.] - ETA: 4s - loss: 8.0617 - acc: 0.5000 - ETA: 4s - loss: 9.8333 - acc: 0.3900 - ETA: 4s - loss: 9.3895 - acc: 0.4125 - ETA: 4s - loss: 8.6242 - acc: 0.4583 - ETA: 4s - loss: 8.5403 - acc: 0.4625 - ETA: 4s - loss: 8.8161 - acc: 0.4425 - ETA: 4s - loss: 8.9137 - acc: 0.4312 - ETA: 4s - loss: 8.9539 - acc: 0.4268 - ETA: 4s - loss: 8.8935 - acc: 0.4328 - ETA: 4s - loss: 8.9182 - acc: 0.4306 - ETA: 4s - loss: 8.8600 - acc: 0.4350 - ETA: 4s - loss: 8.7916 - acc: 0.4398 - ETA: 4s - loss: 8.8485 - acc: 0.4375 - ETA: 4s - loss: 8.8198 - acc: 0.4385 - ETA: 4s - loss: 8.7906 - acc: 0.4393 - ETA: 4s - loss: 8.7870 - acc: 0.4392 - ETA: 4s - loss: 8.8625 - acc: 0.4336 - ETA: 4s - loss: 8.8986 - acc: 0.4324 - ETA: 4s - loss: 8.8672 - acc: 0.4340 - ETA: 3s - loss: 8.8914 - acc: 0.4329 - ETA: 3s - loss: 8.9211 - acc: 0.4316 - ETA: 3s - loss: 8.9961 - acc: 0.4277 - ETA: 3s - loss: 8.9844 - acc: 0.4287 - ETA: 3s - loss: 8.9555 - acc: 0.4308 - ETA: 3s - loss: 9.0113 - acc: 0.4279 - ETA: 3s - loss: 8.9979 - acc: 0.4288 - ETA: 3s - loss: 9.0353 - acc: 0.4252 - ETA: 3s - loss: 9.0208 - acc: 0.4262 - ETA: 3s - loss: 9.0084 - acc: 0.4275 - ETA: 3s - loss: 9.0403 - acc: 0.4259 - ETA: 3s - loss: 9.0906 - acc: 0.4229 - ETA: 3s - loss: 9.0241 - acc: 0.4275 - ETA: 3s - loss: 8.9759 - acc: 0.4302 - ETA: 3s - loss: 9.0235 - acc: 0.4273 - ETA: 3s - loss: 9.0498 - acc: 0.4257 - ETA: 2s - loss: 9.0389 - acc: 0.4268 - ETA: 2s - loss: 9.0389 - acc: 0.4266 - ETA: 2s - loss: 9.0409 - acc: 0.4267 - ETA: 2s - loss: 9.0052 - acc: 0.4282 - ETA: 2s - loss: 8.9960 - acc: 0.4288 - ETA: 2s - loss: 9.0357 - acc: 0.4261 - ETA: 2s - loss: 9.0210 - acc: 0.4261 - ETA: 2s - loss: 9.0721 - acc: 0.4227 - ETA: 2s - loss: 9.0715 - acc: 0.4228 - ETA: 2s - loss: 9.0835 - acc: 0.4219 - ETA: 2s - loss: 9.0586 - acc: 0.4231 - ETA: 2s - loss: 9.0548 - acc: 0.4226 - ETA: 2s - loss: 9.0396 - acc: 0.4230 - ETA: 2s - loss: 9.0401 - acc: 0.4230 - ETA: 2s - loss: 9.0343 - acc: 0.4228 - ETA: 2s - loss: 9.0307 - acc: 0.4226 - ETA: 2s - loss: 9.0239 - acc: 0.4229 - ETA: 1s - loss: 9.0275 - acc: 0.4224 - ETA: 1s - loss: 9.0247 - acc: 0.4220 - ETA: 1s - loss: 9.0309 - acc: 0.4217 - ETA: 1s - loss: 9.0332 - acc: 0.4214 - ETA: 1s - loss: 9.0469 - acc: 0.4204 - ETA: 1s - loss: 9.0518 - acc: 0.4199 - ETA: 1s - loss: 9.0504 - acc: 0.4200 - ETA: 1s - loss: 9.0400 - acc: 0.4210 - ETA: 1s - loss: 9.0309 - acc: 0.4213 - ETA: 1s - loss: 9.0306 - acc: 0.4207 - ETA: 1s - loss: 9.0278 - acc: 0.4204 - ETA: 1s - loss: 9.0386 - acc: 0.4198 - ETA: 1s - loss: 9.0318 - acc: 0.4200 - ETA: 1s - loss: 9.0521 - acc: 0.4187 - ETA: 1s - loss: 9.0426 - acc: 0.4194 - ETA: 1s - loss: 9.0413 - acc: 0.4193 - ETA: 1s - loss: 9.0590 - acc: 0.4180 - ETA: 1s - loss: 9.0623 - acc: 0.4172 - ETA: 0s - loss: 9.0472 - acc: 0.4177 - ETA: 0s - loss: 9.0519 - acc: 0.4174 - ETA: 0s - loss: 9.0583 - acc: 0.4171 - ETA: 0s - loss: 9.0513 - acc: 0.4176 - ETA: 0s - loss: 9.0598 - acc: 0.4171 - ETA: 0s - loss: 9.0627 - acc: 0.4167 - ETA: 0s - loss: 9.0605 - acc: 0.4168 - ETA: 0s - loss: 9.0506 - acc: 0.4175 - ETA: 0s - loss: 9.0400 - acc: 0.4182 - ETA: 0s - loss: 9.0263 - acc: 0.4190 - ETA: 0s - loss: 9.0247 - acc: 0.4192 - ETA: 0s - loss: 9.0143 - acc: 0.4199 - ETA: 0s - loss: 8.9904 - acc: 0.4206 - ETA: 0s - loss: 8.9863 - acc: 0.4207 - ETA: 0s - loss: 8.9869 - acc: 0.4206 - ETA: 0s - loss: 8.9760 - acc: 0.4214 - ETA: 0s - loss: 8.9774 - acc: 0.4215Epoch 00010: val_loss improved from 9.58462 to 9.31507, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.9699 - acc: 0.4217 - val_loss: 9.3151 - val_acc: 0.3545
Epoch 12/20
6600/6680 [============================>.] - ETA: 5s - loss: 9.6809 - acc: 0.4000 - ETA: 4s - loss: 9.4836 - acc: 0.3800 - ETA: 4s - loss: 8.7722 - acc: 0.4333 - ETA: 4s - loss: 8.5701 - acc: 0.4500 - ETA: 4s - loss: 8.5969 - acc: 0.4500 - ETA: 4s - loss: 8.8106 - acc: 0.4357 - ETA: 4s - loss: 8.7555 - acc: 0.4420 - ETA: 4s - loss: 8.7798 - acc: 0.4379 - ETA: 4s - loss: 8.9110 - acc: 0.4303 - ETA: 4s - loss: 8.9031 - acc: 0.4297 - ETA: 4s - loss: 8.7892 - acc: 0.4354 - ETA: 4s - loss: 8.8005 - acc: 0.4356 - ETA: 4s - loss: 8.8071 - acc: 0.4367 - ETA: 4s - loss: 8.7661 - acc: 0.4406 - ETA: 3s - loss: 8.6844 - acc: 0.4456 - ETA: 3s - loss: 8.8784 - acc: 0.4336 - ETA: 3s - loss: 8.9054 - acc: 0.4323 - ETA: 3s - loss: 8.8454 - acc: 0.4368 - ETA: 3s - loss: 8.8272 - acc: 0.4380 - ETA: 3s - loss: 8.8539 - acc: 0.4367 - ETA: 3s - loss: 8.8216 - acc: 0.4386 - ETA: 3s - loss: 8.7938 - acc: 0.4398 - ETA: 3s - loss: 8.7934 - acc: 0.4391 - ETA: 3s - loss: 8.7557 - acc: 0.4406 - ETA: 3s - loss: 8.7360 - acc: 0.4420 - ETA: 3s - loss: 8.6927 - acc: 0.4454 - ETA: 3s - loss: 8.7243 - acc: 0.4436 - ETA: 3s - loss: 8.7753 - acc: 0.4410 - ETA: 3s - loss: 8.7797 - acc: 0.4409 - ETA: 3s - loss: 8.7765 - acc: 0.4412 - ETA: 3s - loss: 8.8366 - acc: 0.4376 - ETA: 3s - loss: 8.8311 - acc: 0.4384 - ETA: 3s - loss: 8.8345 - acc: 0.4384 - ETA: 3s - loss: 8.7974 - acc: 0.4395 - ETA: 3s - loss: 8.8031 - acc: 0.4380 - ETA: 2s - loss: 8.7896 - acc: 0.4380 - ETA: 2s - loss: 8.7457 - acc: 0.4401 - ETA: 2s - loss: 8.7539 - acc: 0.4397 - ETA: 2s - loss: 8.7645 - acc: 0.4386 - ETA: 2s - loss: 8.7632 - acc: 0.4386 - ETA: 2s - loss: 8.7201 - acc: 0.4411 - ETA: 2s - loss: 8.7111 - acc: 0.4413 - ETA: 2s - loss: 8.7022 - acc: 0.4415 - ETA: 2s - loss: 8.7145 - acc: 0.4405 - ETA: 2s - loss: 8.7372 - acc: 0.4393 - ETA: 2s - loss: 8.7476 - acc: 0.4381 - ETA: 2s - loss: 8.7685 - acc: 0.4367 - ETA: 2s - loss: 8.7157 - acc: 0.4395 - ETA: 2s - loss: 8.7164 - acc: 0.4392 - ETA: 2s - loss: 8.6790 - acc: 0.4412 - ETA: 2s - loss: 8.6215 - acc: 0.4452 - ETA: 2s - loss: 8.6238 - acc: 0.4453 - ETA: 1s - loss: 8.6205 - acc: 0.4456 - ETA: 1s - loss: 8.6517 - acc: 0.4437 - ETA: 1s - loss: 8.6556 - acc: 0.4436 - ETA: 1s - loss: 8.6525 - acc: 0.4439 - ETA: 1s - loss: 8.6514 - acc: 0.4440 - ETA: 1s - loss: 8.6541 - acc: 0.4435 - ETA: 1s - loss: 8.6257 - acc: 0.4454 - ETA: 1s - loss: 8.6179 - acc: 0.4457 - ETA: 1s - loss: 8.6149 - acc: 0.4455 - ETA: 1s - loss: 8.6046 - acc: 0.4462 - ETA: 1s - loss: 8.5937 - acc: 0.4467 - ETA: 1s - loss: 8.5874 - acc: 0.4467 - ETA: 1s - loss: 8.6070 - acc: 0.4452 - ETA: 1s - loss: 8.5912 - acc: 0.4461 - ETA: 1s - loss: 8.5865 - acc: 0.4465 - ETA: 1s - loss: 8.6051 - acc: 0.4452 - ETA: 1s - loss: 8.6401 - acc: 0.4430 - ETA: 0s - loss: 8.6556 - acc: 0.4424 - ETA: 0s - loss: 8.6517 - acc: 0.4422 - ETA: 0s - loss: 8.6289 - acc: 0.4439 - ETA: 0s - loss: 8.6287 - acc: 0.4440 - ETA: 0s - loss: 8.6154 - acc: 0.4449 - ETA: 0s - loss: 8.6280 - acc: 0.4443 - ETA: 0s - loss: 8.6370 - acc: 0.4439 - ETA: 0s - loss: 8.6192 - acc: 0.4451 - ETA: 0s - loss: 8.6141 - acc: 0.4452 - ETA: 0s - loss: 8.5971 - acc: 0.4462 - ETA: 0s - loss: 8.5930 - acc: 0.4460 - ETA: 0s - loss: 8.5987 - acc: 0.4454 - ETA: 0s - loss: 8.6011 - acc: 0.4448 - ETA: 0s - loss: 8.5956 - acc: 0.4452 - ETA: 0s - loss: 8.6025 - acc: 0.4446 - ETA: 0s - loss: 8.6043 - acc: 0.4445Epoch 00011: val_loss improved from 9.31507 to 9.06893, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.6128 - acc: 0.4439 - val_loss: 9.0689 - val_acc: 0.3665
Epoch 13/20
6600/6680 [============================>.] - ETA: 4s - loss: 7.7143 - acc: 0.5000 - ETA: 5s - loss: 8.9258 - acc: 0.4400 - ETA: 4s - loss: 9.0284 - acc: 0.4278 - ETA: 4s - loss: 8.9164 - acc: 0.4385 - ETA: 4s - loss: 8.6553 - acc: 0.4441 - ETA: 4s - loss: 8.7410 - acc: 0.4381 - ETA: 4s - loss: 8.6339 - acc: 0.4460 - ETA: 4s - loss: 8.5838 - acc: 0.4517 - ETA: 4s - loss: 8.5054 - acc: 0.4545 - ETA: 4s - loss: 8.5056 - acc: 0.4541 - ETA: 4s - loss: 8.5023 - acc: 0.4561 - ETA: 4s - loss: 8.5913 - acc: 0.4500 - ETA: 4s - loss: 8.6468 - acc: 0.4480 - ETA: 4s - loss: 8.5432 - acc: 0.4519 - ETA: 4s - loss: 8.5377 - acc: 0.4509 - ETA: 4s - loss: 8.5151 - acc: 0.4525 - ETA: 4s - loss: 8.4643 - acc: 0.4562 - ETA: 4s - loss: 8.4585 - acc: 0.4574 - ETA: 4s - loss: 8.4265 - acc: 0.4597 - ETA: 3s - loss: 8.4867 - acc: 0.4559 - ETA: 3s - loss: 8.4153 - acc: 0.4613 - ETA: 3s - loss: 8.4638 - acc: 0.4589 - ETA: 3s - loss: 8.4747 - acc: 0.4585 - ETA: 3s - loss: 8.4600 - acc: 0.4592 - ETA: 3s - loss: 8.4860 - acc: 0.4578 - ETA: 3s - loss: 8.5415 - acc: 0.4550 - ETA: 3s - loss: 8.5550 - acc: 0.4543 - ETA: 3s - loss: 8.5334 - acc: 0.4551 - ETA: 3s - loss: 8.5325 - acc: 0.4554 - ETA: 3s - loss: 8.5234 - acc: 0.4565 - ETA: 3s - loss: 8.5057 - acc: 0.4575 - ETA: 3s - loss: 8.4787 - acc: 0.4593 - ETA: 3s - loss: 8.5075 - acc: 0.4570 - ETA: 3s - loss: 8.5052 - acc: 0.4569 - ETA: 3s - loss: 8.5206 - acc: 0.4560 - ETA: 2s - loss: 8.4987 - acc: 0.4577 - ETA: 2s - loss: 8.4832 - acc: 0.4589 - ETA: 2s - loss: 8.4672 - acc: 0.4600 - ETA: 2s - loss: 8.4573 - acc: 0.4594 - ETA: 2s - loss: 8.4166 - acc: 0.4621 - ETA: 2s - loss: 8.4034 - acc: 0.4631 - ETA: 2s - loss: 8.4114 - acc: 0.4627 - ETA: 2s - loss: 8.4005 - acc: 0.4630 - ETA: 2s - loss: 8.4233 - acc: 0.4618 - ETA: 2s - loss: 8.4488 - acc: 0.4601 - ETA: 2s - loss: 8.4638 - acc: 0.4590 - ETA: 2s - loss: 8.4392 - acc: 0.4605 - ETA: 2s - loss: 8.4530 - acc: 0.4600 - ETA: 2s - loss: 8.4412 - acc: 0.4606 - ETA: 2s - loss: 8.4644 - acc: 0.4594 - ETA: 2s - loss: 8.4624 - acc: 0.4598 - ETA: 2s - loss: 8.4717 - acc: 0.4589 - ETA: 2s - loss: 8.4627 - acc: 0.4595 - ETA: 2s - loss: 8.4749 - acc: 0.4589 - ETA: 1s - loss: 8.4504 - acc: 0.4602 - ETA: 1s - loss: 8.4347 - acc: 0.4610 - ETA: 1s - loss: 8.4298 - acc: 0.4613 - ETA: 1s - loss: 8.4345 - acc: 0.4611 - ETA: 1s - loss: 8.4223 - acc: 0.4618 - ETA: 1s - loss: 8.4320 - acc: 0.4614 - ETA: 1s - loss: 8.4631 - acc: 0.4592 - ETA: 1s - loss: 8.4612 - acc: 0.4593 - ETA: 1s - loss: 8.4485 - acc: 0.4602 - ETA: 1s - loss: 8.4556 - acc: 0.4596 - ETA: 1s - loss: 8.4677 - acc: 0.4590 - ETA: 1s - loss: 8.4771 - acc: 0.4585 - ETA: 1s - loss: 8.4801 - acc: 0.4581 - ETA: 1s - loss: 8.4771 - acc: 0.4584 - ETA: 1s - loss: 8.4782 - acc: 0.4585 - ETA: 1s - loss: 8.4827 - acc: 0.4578 - ETA: 1s - loss: 8.4967 - acc: 0.4568 - ETA: 1s - loss: 8.5043 - acc: 0.4561 - ETA: 0s - loss: 8.5044 - acc: 0.4562 - ETA: 0s - loss: 8.4829 - acc: 0.4574 - ETA: 0s - loss: 8.4843 - acc: 0.4573 - ETA: 0s - loss: 8.4967 - acc: 0.4565 - ETA: 0s - loss: 8.4991 - acc: 0.4566 - ETA: 0s - loss: 8.4745 - acc: 0.4580 - ETA: 0s - loss: 8.4622 - acc: 0.4588 - ETA: 0s - loss: 8.4754 - acc: 0.4578 - ETA: 0s - loss: 8.4964 - acc: 0.4567 - ETA: 0s - loss: 8.4880 - acc: 0.4573 - ETA: 0s - loss: 8.4844 - acc: 0.4574 - ETA: 0s - loss: 8.4932 - acc: 0.4570 - ETA: 0s - loss: 8.4942 - acc: 0.4571 - ETA: 0s - loss: 8.4821 - acc: 0.4576 - ETA: 0s - loss: 8.4801 - acc: 0.4575 - ETA: 0s - loss: 8.4878 - acc: 0.4570Epoch 00012: val_loss improved from 9.06893 to 8.98390, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.4885 - acc: 0.4566 - val_loss: 8.9839 - val_acc: 0.3784
Epoch 14/20
6600/6680 [============================>.] - ETA: 4s - loss: 6.4533 - acc: 0.6000 - ETA: 4s - loss: 8.4656 - acc: 0.4700 - ETA: 4s - loss: 9.2190 - acc: 0.4222 - ETA: 4s - loss: 8.8675 - acc: 0.4423 - ETA: 4s - loss: 8.5000 - acc: 0.4647 - ETA: 4s - loss: 8.6852 - acc: 0.4548 - ETA: 4s - loss: 8.7528 - acc: 0.4480 - ETA: 4s - loss: 8.7437 - acc: 0.4483 - ETA: 4s - loss: 8.6547 - acc: 0.4545 - ETA: 4s - loss: 8.6541 - acc: 0.4527 - ETA: 4s - loss: 8.6583 - acc: 0.4524 - ETA: 4s - loss: 8.6568 - acc: 0.4511 - ETA: 4s - loss: 8.6611 - acc: 0.4510 - ETA: 4s - loss: 8.6740 - acc: 0.4510 - ETA: 4s - loss: 8.6704 - acc: 0.4519 - ETA: 4s - loss: 8.6476 - acc: 0.4534 - ETA: 4s - loss: 8.5738 - acc: 0.4581 - ETA: 4s - loss: 8.5568 - acc: 0.4591 - ETA: 4s - loss: 8.5422 - acc: 0.4586 - ETA: 4s - loss: 8.5000 - acc: 0.4615 - ETA: 3s - loss: 8.4409 - acc: 0.4654 - ETA: 3s - loss: 8.4620 - acc: 0.4646 - ETA: 3s - loss: 8.4621 - acc: 0.4651 - ETA: 3s - loss: 8.4732 - acc: 0.4639 - ETA: 3s - loss: 8.4000 - acc: 0.4683 - ETA: 3s - loss: 8.4405 - acc: 0.4660 - ETA: 3s - loss: 8.4573 - acc: 0.4645 - ETA: 3s - loss: 8.3941 - acc: 0.4678 - ETA: 3s - loss: 8.3780 - acc: 0.4681 - ETA: 3s - loss: 8.3550 - acc: 0.4698 - ETA: 3s - loss: 8.4010 - acc: 0.4662 - ETA: 3s - loss: 8.3894 - acc: 0.4669 - ETA: 3s - loss: 8.3635 - acc: 0.4676 - ETA: 3s - loss: 8.3352 - acc: 0.4694 - ETA: 3s - loss: 8.3277 - acc: 0.4700 - ETA: 3s - loss: 8.3641 - acc: 0.4680 - ETA: 3s - loss: 8.3386 - acc: 0.4693 - ETA: 3s - loss: 8.3475 - acc: 0.4688 - ETA: 2s - loss: 8.3762 - acc: 0.4672 - ETA: 2s - loss: 8.3464 - acc: 0.4691 - ETA: 2s - loss: 8.3813 - acc: 0.4673 - ETA: 2s - loss: 8.3680 - acc: 0.4685 - ETA: 2s - loss: 8.3712 - acc: 0.4683 - ETA: 2s - loss: 8.3417 - acc: 0.4700 - ETA: 2s - loss: 8.3176 - acc: 0.4717 - ETA: 2s - loss: 8.3306 - acc: 0.4709 - ETA: 2s - loss: 8.3363 - acc: 0.4705 - ETA: 2s - loss: 8.3544 - acc: 0.4692 - ETA: 2s - loss: 8.3594 - acc: 0.4689 - ETA: 2s - loss: 8.3811 - acc: 0.4675 - ETA: 2s - loss: 8.3713 - acc: 0.4680 - ETA: 2s - loss: 8.3947 - acc: 0.4663 - ETA: 2s - loss: 8.4166 - acc: 0.4652 - ETA: 2s - loss: 8.4404 - acc: 0.4639 - ETA: 2s - loss: 8.4313 - acc: 0.4641 - ETA: 1s - loss: 8.4286 - acc: 0.4644 - ETA: 1s - loss: 8.4412 - acc: 0.4636 - ETA: 1s - loss: 8.4068 - acc: 0.4654 - ETA: 1s - loss: 8.4050 - acc: 0.4656 - ETA: 1s - loss: 8.3920 - acc: 0.4664 - ETA: 1s - loss: 8.3645 - acc: 0.4675 - ETA: 1s - loss: 8.3460 - acc: 0.4687 - ETA: 1s - loss: 8.3372 - acc: 0.4690 - ETA: 1s - loss: 8.3376 - acc: 0.4691 - ETA: 1s - loss: 8.3366 - acc: 0.4692 - ETA: 1s - loss: 8.3394 - acc: 0.4691 - ETA: 1s - loss: 8.3500 - acc: 0.4684 - ETA: 1s - loss: 8.3611 - acc: 0.4679 - ETA: 1s - loss: 8.3291 - acc: 0.4699 - ETA: 1s - loss: 8.3421 - acc: 0.4691 - ETA: 0s - loss: 8.3654 - acc: 0.4675 - ETA: 0s - loss: 8.3826 - acc: 0.4663 - ETA: 0s - loss: 8.3833 - acc: 0.4664 - ETA: 0s - loss: 8.3991 - acc: 0.4657 - ETA: 0s - loss: 8.3977 - acc: 0.4658 - ETA: 0s - loss: 8.3871 - acc: 0.4663 - ETA: 0s - loss: 8.3810 - acc: 0.4668 - ETA: 0s - loss: 8.3867 - acc: 0.4665 - ETA: 0s - loss: 8.3890 - acc: 0.4665 - ETA: 0s - loss: 8.3830 - acc: 0.4670 - ETA: 0s - loss: 8.3843 - acc: 0.4665 - ETA: 0s - loss: 8.3660 - acc: 0.4676 - ETA: 0s - loss: 8.3685 - acc: 0.4675 - ETA: 0s - loss: 8.3794 - acc: 0.4667 - ETA: 0s - loss: 8.3837 - acc: 0.4665 - ETA: 0s - loss: 8.4030 - acc: 0.4652 - ETA: 0s - loss: 8.4108 - acc: 0.4646 - ETA: 0s - loss: 8.4122 - acc: 0.4640 - ETA: 0s - loss: 8.4109 - acc: 0.4639Epoch 00013: val_loss improved from 8.98390 to 8.93235, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.3971 - acc: 0.4650 - val_loss: 8.9324 - val_acc: 0.3892
Epoch 15/20
6660/6680 [============================>.] - ETA: 5s - loss: 8.8761 - acc: 0.4500 - ETA: 4s - loss: 7.9955 - acc: 0.4900 - ETA: 4s - loss: 8.9197 - acc: 0.4389 - ETA: 4s - loss: 8.2273 - acc: 0.4808 - ETA: 4s - loss: 8.0542 - acc: 0.4912 - ETA: 4s - loss: 8.4391 - acc: 0.4690 - ETA: 4s - loss: 8.3786 - acc: 0.4740 - ETA: 4s - loss: 8.2512 - acc: 0.4828 - ETA: 4s - loss: 8.1817 - acc: 0.4864 - ETA: 4s - loss: 8.1114 - acc: 0.4892 - ETA: 4s - loss: 8.1307 - acc: 0.4878 - ETA: 4s - loss: 8.2012 - acc: 0.4830 - ETA: 4s - loss: 8.3089 - acc: 0.4760 - ETA: 4s - loss: 8.2057 - acc: 0.4817 - ETA: 4s - loss: 8.2280 - acc: 0.4795 - ETA: 4s - loss: 8.2189 - acc: 0.4792 - ETA: 4s - loss: 8.2864 - acc: 0.4750 - ETA: 4s - loss: 8.2971 - acc: 0.4750 - ETA: 4s - loss: 8.3069 - acc: 0.4750 - ETA: 3s - loss: 8.3898 - acc: 0.4704 - ETA: 3s - loss: 8.3898 - acc: 0.4696 - ETA: 3s - loss: 8.3969 - acc: 0.4695 - ETA: 3s - loss: 8.3920 - acc: 0.4698 - ETA: 3s - loss: 8.3002 - acc: 0.4756 - ETA: 3s - loss: 8.2927 - acc: 0.4763 - ETA: 3s - loss: 8.2499 - acc: 0.4794 - ETA: 3s - loss: 8.2666 - acc: 0.4787 - ETA: 3s - loss: 8.2761 - acc: 0.4784 - ETA: 3s - loss: 8.2628 - acc: 0.4794 - ETA: 3s - loss: 8.2211 - acc: 0.4820 - ETA: 3s - loss: 8.2122 - acc: 0.4822 - ETA: 3s - loss: 8.2425 - acc: 0.4803 - ETA: 3s - loss: 8.2426 - acc: 0.4805 - ETA: 3s - loss: 8.2876 - acc: 0.4780 - ETA: 3s - loss: 8.3004 - acc: 0.4767 - ETA: 3s - loss: 8.3054 - acc: 0.4767 - ETA: 3s - loss: 8.3107 - acc: 0.4763 - ETA: 2s - loss: 8.3206 - acc: 0.4759 - ETA: 2s - loss: 8.3025 - acc: 0.4772 - ETA: 2s - loss: 8.2758 - acc: 0.4788 - ETA: 2s - loss: 8.3014 - acc: 0.4774 - ETA: 2s - loss: 8.3267 - acc: 0.4758 - ETA: 2s - loss: 8.3255 - acc: 0.4758 - ETA: 2s - loss: 8.3529 - acc: 0.4743 - ETA: 2s - loss: 8.3562 - acc: 0.4740 - ETA: 2s - loss: 8.3318 - acc: 0.4751 - ETA: 2s - loss: 8.2948 - acc: 0.4771 - ETA: 2s - loss: 8.2664 - acc: 0.4790 - ETA: 2s - loss: 8.2717 - acc: 0.4786 - ETA: 2s - loss: 8.2674 - acc: 0.4791 - ETA: 2s - loss: 8.3256 - acc: 0.4754 - ETA: 2s - loss: 8.3213 - acc: 0.4756 - ETA: 1s - loss: 8.3209 - acc: 0.4754 - ETA: 1s - loss: 8.3047 - acc: 0.4763 - ETA: 1s - loss: 8.2970 - acc: 0.4768 - ETA: 1s - loss: 8.3073 - acc: 0.4758 - ETA: 1s - loss: 8.3053 - acc: 0.4756 - ETA: 1s - loss: 8.3210 - acc: 0.4747 - ETA: 1s - loss: 8.3113 - acc: 0.4751 - ETA: 1s - loss: 8.3214 - acc: 0.4745 - ETA: 1s - loss: 8.3170 - acc: 0.4749 - ETA: 1s - loss: 8.3310 - acc: 0.4741 - ETA: 1s - loss: 8.3431 - acc: 0.4735 - ETA: 1s - loss: 8.3334 - acc: 0.4740 - ETA: 1s - loss: 8.3467 - acc: 0.4731 - ETA: 1s - loss: 8.3306 - acc: 0.4742 - ETA: 1s - loss: 8.3369 - acc: 0.4738 - ETA: 1s - loss: 8.3395 - acc: 0.4737 - ETA: 1s - loss: 8.3262 - acc: 0.4746 - ETA: 1s - loss: 8.3192 - acc: 0.4752 - ETA: 0s - loss: 8.3124 - acc: 0.4757 - ETA: 0s - loss: 8.3294 - acc: 0.4746 - ETA: 0s - loss: 8.3121 - acc: 0.4758 - ETA: 0s - loss: 8.3087 - acc: 0.4760 - ETA: 0s - loss: 8.3115 - acc: 0.4756 - ETA: 0s - loss: 8.3280 - acc: 0.4744 - ETA: 0s - loss: 8.3380 - acc: 0.4739 - ETA: 0s - loss: 8.3666 - acc: 0.4722 - ETA: 0s - loss: 8.3832 - acc: 0.4711 - ETA: 0s - loss: 8.3963 - acc: 0.4701 - ETA: 0s - loss: 8.3905 - acc: 0.4706 - ETA: 0s - loss: 8.3815 - acc: 0.4711 - ETA: 0s - loss: 8.3663 - acc: 0.4719 - ETA: 0s - loss: 8.3707 - acc: 0.4713 - ETA: 0s - loss: 8.3578 - acc: 0.4718 - ETA: 0s - loss: 8.3591 - acc: 0.4719 - ETA: 0s - loss: 8.3483 - acc: 0.4727Epoch 00014: val_loss improved from 8.93235 to 8.89796, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.3430 - acc: 0.4729 - val_loss: 8.8980 - val_acc: 0.3880
Epoch 16/20
6660/6680 [============================>.] - ETA: 5s - loss: 4.8421 - acc: 0.7000 - ETA: 5s - loss: 7.0944 - acc: 0.5600 - ETA: 4s - loss: 7.7026 - acc: 0.5222 - ETA: 4s - loss: 8.0637 - acc: 0.4962 - ETA: 4s - loss: 8.0455 - acc: 0.4971 - ETA: 4s - loss: 8.1252 - acc: 0.4929 - ETA: 4s - loss: 8.0171 - acc: 0.5000 - ETA: 4s - loss: 7.9736 - acc: 0.5000 - ETA: 4s - loss: 7.9830 - acc: 0.5000 - ETA: 4s - loss: 7.8680 - acc: 0.5076 - ETA: 4s - loss: 7.6336 - acc: 0.5203 - ETA: 4s - loss: 7.7374 - acc: 0.5134 - ETA: 4s - loss: 7.8054 - acc: 0.5089 - ETA: 4s - loss: 7.8100 - acc: 0.5092 - ETA: 4s - loss: 7.8288 - acc: 0.5085 - ETA: 4s - loss: 7.9299 - acc: 0.5026 - ETA: 4s - loss: 7.9498 - acc: 0.5017 - ETA: 4s - loss: 8.0071 - acc: 0.4984 - ETA: 4s - loss: 8.0339 - acc: 0.4971 - ETA: 4s - loss: 8.0810 - acc: 0.4944 - ETA: 4s - loss: 8.2072 - acc: 0.4868 - ETA: 3s - loss: 8.2100 - acc: 0.4869 - ETA: 3s - loss: 8.2319 - acc: 0.4857 - ETA: 3s - loss: 8.1802 - acc: 0.4886 - ETA: 3s - loss: 8.2098 - acc: 0.4870 - ETA: 3s - loss: 8.2058 - acc: 0.4865 - ETA: 3s - loss: 8.2046 - acc: 0.4865 - ETA: 3s - loss: 8.1917 - acc: 0.4875 - ETA: 3s - loss: 8.1868 - acc: 0.4880 - ETA: 3s - loss: 8.1752 - acc: 0.4888 - ETA: 3s - loss: 8.0981 - acc: 0.4927 - ETA: 3s - loss: 8.1574 - acc: 0.4892 - ETA: 3s - loss: 8.1478 - acc: 0.4899 - ETA: 3s - loss: 8.1874 - acc: 0.4867 - ETA: 3s - loss: 8.1915 - acc: 0.4860 - ETA: 2s - loss: 8.2181 - acc: 0.4842 - ETA: 2s - loss: 8.1857 - acc: 0.4861 - ETA: 2s - loss: 8.1845 - acc: 0.4861 - ETA: 2s - loss: 8.1847 - acc: 0.4861 - ETA: 2s - loss: 8.1851 - acc: 0.4859 - ETA: 2s - loss: 8.1895 - acc: 0.4853 - ETA: 2s - loss: 8.1963 - acc: 0.4847 - ETA: 2s - loss: 8.2590 - acc: 0.4808 - ETA: 2s - loss: 8.2539 - acc: 0.4804 - ETA: 2s - loss: 8.2549 - acc: 0.4795 - ETA: 2s - loss: 8.2656 - acc: 0.4787 - ETA: 2s - loss: 8.2624 - acc: 0.4788 - ETA: 2s - loss: 8.2761 - acc: 0.4779 - ETA: 2s - loss: 8.2674 - acc: 0.4784 - ETA: 2s - loss: 8.2773 - acc: 0.4777 - ETA: 2s - loss: 8.2882 - acc: 0.4770 - ETA: 2s - loss: 8.2832 - acc: 0.4771 - ETA: 2s - loss: 8.3046 - acc: 0.4756 - ETA: 2s - loss: 8.2879 - acc: 0.4765 - ETA: 2s - loss: 8.2904 - acc: 0.4761 - ETA: 1s - loss: 8.3067 - acc: 0.4752 - ETA: 1s - loss: 8.3134 - acc: 0.4744 - ETA: 1s - loss: 8.3013 - acc: 0.4754 - ETA: 1s - loss: 8.3004 - acc: 0.4753 - ETA: 1s - loss: 8.2981 - acc: 0.4756 - ETA: 1s - loss: 8.3070 - acc: 0.4751 - ETA: 1s - loss: 8.3027 - acc: 0.4755 - ETA: 1s - loss: 8.3044 - acc: 0.4753 - ETA: 1s - loss: 8.2768 - acc: 0.4768 - ETA: 1s - loss: 8.2800 - acc: 0.4761 - ETA: 1s - loss: 8.2786 - acc: 0.4759 - ETA: 1s - loss: 8.2626 - acc: 0.4765 - ETA: 1s - loss: 8.2700 - acc: 0.4759 - ETA: 1s - loss: 8.2526 - acc: 0.4768 - ETA: 1s - loss: 8.2537 - acc: 0.4768 - ETA: 1s - loss: 8.2422 - acc: 0.4772 - ETA: 0s - loss: 8.2609 - acc: 0.4758 - ETA: 0s - loss: 8.2560 - acc: 0.4762 - ETA: 0s - loss: 8.2476 - acc: 0.4767 - ETA: 0s - loss: 8.2578 - acc: 0.4761 - ETA: 0s - loss: 8.2517 - acc: 0.4765 - ETA: 0s - loss: 8.2596 - acc: 0.4761 - ETA: 0s - loss: 8.2570 - acc: 0.4761 - ETA: 0s - loss: 8.2448 - acc: 0.4766 - ETA: 0s - loss: 8.2600 - acc: 0.4753 - ETA: 0s - loss: 8.2471 - acc: 0.4760 - ETA: 0s - loss: 8.2717 - acc: 0.4746 - ETA: 0s - loss: 8.2791 - acc: 0.4739 - ETA: 0s - loss: 8.2959 - acc: 0.4729 - ETA: 0s - loss: 8.2987 - acc: 0.4729 - ETA: 0s - loss: 8.3014 - acc: 0.4723 - ETA: 0s - loss: 8.2938 - acc: 0.4728 - ETA: 0s - loss: 8.2765 - acc: 0.4738 - ETA: 0s - loss: 8.2897 - acc: 0.4729 - ETA: 0s - loss: 8.2889 - acc: 0.4728Epoch 00015: val_loss improved from 8.89796 to 8.81633, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.2858 - acc: 0.4731 - val_loss: 8.8163 - val_acc: 0.3940
Epoch 17/20
6620/6680 [============================>.] - ETA: 5s - loss: 8.0591 - acc: 0.5000 - ETA: 5s - loss: 7.8939 - acc: 0.4800 - ETA: 5s - loss: 7.2912 - acc: 0.5250 - ETA: 5s - loss: 8.3572 - acc: 0.4667 - ETA: 4s - loss: 8.3704 - acc: 0.4688 - ETA: 4s - loss: 8.4357 - acc: 0.4650 - ETA: 4s - loss: 8.3528 - acc: 0.4687 - ETA: 4s - loss: 8.4414 - acc: 0.4630 - ETA: 4s - loss: 8.4454 - acc: 0.4629 - ETA: 4s - loss: 8.3765 - acc: 0.4643 - ETA: 4s - loss: 8.4066 - acc: 0.4603 - ETA: 4s - loss: 8.4025 - acc: 0.4605 - ETA: 4s - loss: 8.3239 - acc: 0.4660 - ETA: 4s - loss: 8.2300 - acc: 0.4706 - ETA: 4s - loss: 8.2059 - acc: 0.4709 - ETA: 4s - loss: 8.2230 - acc: 0.4678 - ETA: 3s - loss: 8.2701 - acc: 0.4643 - ETA: 3s - loss: 8.2158 - acc: 0.4679 - ETA: 3s - loss: 8.2270 - acc: 0.4683 - ETA: 3s - loss: 8.2225 - acc: 0.4693 - ETA: 3s - loss: 8.2554 - acc: 0.4684 - ETA: 3s - loss: 8.2077 - acc: 0.4723 - ETA: 3s - loss: 8.1456 - acc: 0.4770 - ETA: 3s - loss: 8.1329 - acc: 0.4786 - ETA: 3s - loss: 8.1497 - acc: 0.4779 - ETA: 3s - loss: 8.1658 - acc: 0.4763 - ETA: 3s - loss: 8.1320 - acc: 0.4786 - ETA: 3s - loss: 8.1330 - acc: 0.4780 - ETA: 3s - loss: 8.0543 - acc: 0.4833 - ETA: 3s - loss: 8.0979 - acc: 0.4804 - ETA: 3s - loss: 8.0830 - acc: 0.4811 - ETA: 3s - loss: 8.0612 - acc: 0.4825 - ETA: 2s - loss: 8.0580 - acc: 0.4831 - ETA: 2s - loss: 8.0667 - acc: 0.4824 - ETA: 2s - loss: 8.0605 - acc: 0.4822 - ETA: 2s - loss: 8.0195 - acc: 0.4845 - ETA: 2s - loss: 8.0208 - acc: 0.4850 - ETA: 2s - loss: 8.0495 - acc: 0.4837 - ETA: 2s - loss: 8.0817 - acc: 0.4813 - ETA: 2s - loss: 8.0815 - acc: 0.4818 - ETA: 2s - loss: 8.0876 - acc: 0.4810 - ETA: 2s - loss: 8.1055 - acc: 0.4799 - ETA: 2s - loss: 8.1001 - acc: 0.4804 - ETA: 2s - loss: 8.0716 - acc: 0.4821 - ETA: 2s - loss: 8.0864 - acc: 0.4812 - ETA: 2s - loss: 8.1166 - acc: 0.4791 - ETA: 2s - loss: 8.1106 - acc: 0.4796 - ETA: 2s - loss: 8.1039 - acc: 0.4803 - ETA: 2s - loss: 8.1221 - acc: 0.4791 - ETA: 2s - loss: 8.1131 - acc: 0.4798 - ETA: 2s - loss: 8.1181 - acc: 0.4797 - ETA: 1s - loss: 8.1230 - acc: 0.4789 - ETA: 1s - loss: 8.1032 - acc: 0.4802 - ETA: 1s - loss: 8.1315 - acc: 0.4787 - ETA: 1s - loss: 8.1200 - acc: 0.4793 - ETA: 1s - loss: 8.1238 - acc: 0.4790 - ETA: 1s - loss: 8.1220 - acc: 0.4787 - ETA: 1s - loss: 8.1050 - acc: 0.4799 - ETA: 1s - loss: 8.0905 - acc: 0.4809 - ETA: 1s - loss: 8.0994 - acc: 0.4805 - ETA: 1s - loss: 8.1038 - acc: 0.4803 - ETA: 1s - loss: 8.0911 - acc: 0.4809 - ETA: 1s - loss: 8.1051 - acc: 0.4802 - ETA: 1s - loss: 8.1047 - acc: 0.4802 - ETA: 1s - loss: 8.0832 - acc: 0.4809 - ETA: 1s - loss: 8.0904 - acc: 0.4804 - ETA: 1s - loss: 8.0989 - acc: 0.4796 - ETA: 1s - loss: 8.1124 - acc: 0.4784 - ETA: 1s - loss: 8.0885 - acc: 0.4800 - ETA: 0s - loss: 8.0944 - acc: 0.4797 - ETA: 0s - loss: 8.0920 - acc: 0.4800 - ETA: 0s - loss: 8.0899 - acc: 0.4800 - ETA: 0s - loss: 8.0855 - acc: 0.4801 - ETA: 0s - loss: 8.0966 - acc: 0.4796 - ETA: 0s - loss: 8.0878 - acc: 0.4801 - ETA: 0s - loss: 8.0692 - acc: 0.4811 - ETA: 0s - loss: 8.0801 - acc: 0.4806 - ETA: 0s - loss: 8.0704 - acc: 0.4812 - ETA: 0s - loss: 8.0369 - acc: 0.4832 - ETA: 0s - loss: 8.0377 - acc: 0.4831 - ETA: 0s - loss: 8.0337 - acc: 0.4831 - ETA: 0s - loss: 8.0268 - acc: 0.4837 - ETA: 0s - loss: 8.0329 - acc: 0.4828 - ETA: 0s - loss: 8.0306 - acc: 0.4828 - ETA: 0s - loss: 8.0195 - acc: 0.4835 - ETA: 0s - loss: 8.0356 - acc: 0.4826Epoch 00016: val_loss improved from 8.81633 to 8.58997, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 8.0386 - acc: 0.4825 - val_loss: 8.5900 - val_acc: 0.4048
Epoch 18/20
6660/6680 [============================>.] - ETA: 6s - loss: 6.8748 - acc: 0.5500 - ETA: 5s - loss: 7.5360 - acc: 0.5100 - ETA: 5s - loss: 8.3320 - acc: 0.4667 - ETA: 5s - loss: 8.4999 - acc: 0.4583 - ETA: 5s - loss: 7.9693 - acc: 0.4781 - ETA: 4s - loss: 7.9241 - acc: 0.4850 - ETA: 4s - loss: 8.1534 - acc: 0.4729 - ETA: 4s - loss: 8.1167 - acc: 0.4750 - ETA: 4s - loss: 8.0844 - acc: 0.4797 - ETA: 4s - loss: 7.9526 - acc: 0.4875 - ETA: 4s - loss: 7.8853 - acc: 0.4925 - ETA: 4s - loss: 7.9433 - acc: 0.4886 - ETA: 4s - loss: 8.1209 - acc: 0.4792 - ETA: 4s - loss: 8.0924 - acc: 0.4808 - ETA: 3s - loss: 8.1087 - acc: 0.4795 - ETA: 3s - loss: 8.0404 - acc: 0.4842 - ETA: 3s - loss: 8.0671 - acc: 0.4836 - ETA: 3s - loss: 8.0769 - acc: 0.4838 - ETA: 3s - loss: 7.9921 - acc: 0.4875 - ETA: 3s - loss: 8.0279 - acc: 0.4862 - ETA: 3s - loss: 7.9600 - acc: 0.4894 - ETA: 3s - loss: 7.9366 - acc: 0.4917 - ETA: 3s - loss: 7.9170 - acc: 0.4932 - ETA: 3s - loss: 8.0064 - acc: 0.4880 - ETA: 3s - loss: 7.9497 - acc: 0.4917 - ETA: 3s - loss: 7.9200 - acc: 0.4930 - ETA: 3s - loss: 7.8993 - acc: 0.4947 - ETA: 3s - loss: 7.9100 - acc: 0.4935 - ETA: 3s - loss: 7.8753 - acc: 0.4960 - ETA: 3s - loss: 7.8850 - acc: 0.4953 - ETA: 3s - loss: 7.8913 - acc: 0.4950 - ETA: 3s - loss: 7.8551 - acc: 0.4968 - ETA: 2s - loss: 7.8503 - acc: 0.4973 - ETA: 2s - loss: 7.8811 - acc: 0.4958 - ETA: 2s - loss: 7.8900 - acc: 0.4949 - ETA: 2s - loss: 7.8950 - acc: 0.4950 - ETA: 2s - loss: 7.9450 - acc: 0.4917 - ETA: 2s - loss: 7.9488 - acc: 0.4915 - ETA: 2s - loss: 7.9496 - acc: 0.4917 - ETA: 2s - loss: 7.9578 - acc: 0.4912 - ETA: 2s - loss: 7.9675 - acc: 0.4908 - ETA: 2s - loss: 7.9390 - acc: 0.4928 - ETA: 2s - loss: 7.9282 - acc: 0.4930 - ETA: 2s - loss: 7.9480 - acc: 0.4917 - ETA: 2s - loss: 7.9613 - acc: 0.4907 - ETA: 2s - loss: 7.9547 - acc: 0.4912 - ETA: 2s - loss: 7.9324 - acc: 0.4922 - ETA: 2s - loss: 7.9138 - acc: 0.4935 - ETA: 2s - loss: 7.9489 - acc: 0.4912 - ETA: 2s - loss: 7.9208 - acc: 0.4930 - ETA: 2s - loss: 7.9277 - acc: 0.4929 - ETA: 1s - loss: 7.9207 - acc: 0.4935 - ETA: 1s - loss: 7.8950 - acc: 0.4951 - ETA: 1s - loss: 7.9148 - acc: 0.4940 - ETA: 1s - loss: 7.9042 - acc: 0.4948 - ETA: 1s - loss: 7.8878 - acc: 0.4960 - ETA: 1s - loss: 7.8596 - acc: 0.4977 - ETA: 1s - loss: 7.8652 - acc: 0.4971 - ETA: 1s - loss: 7.8894 - acc: 0.4956 - ETA: 1s - loss: 7.8960 - acc: 0.4954 - ETA: 1s - loss: 7.9116 - acc: 0.4944 - ETA: 1s - loss: 7.8880 - acc: 0.4960 - ETA: 1s - loss: 7.8957 - acc: 0.4952 - ETA: 1s - loss: 7.8820 - acc: 0.4963 - ETA: 1s - loss: 7.8755 - acc: 0.4968 - ETA: 1s - loss: 7.8597 - acc: 0.4978 - ETA: 1s - loss: 7.8415 - acc: 0.4990 - ETA: 1s - loss: 7.8332 - acc: 0.4996 - ETA: 1s - loss: 7.8313 - acc: 0.4996 - ETA: 0s - loss: 7.8518 - acc: 0.4983 - ETA: 0s - loss: 7.8703 - acc: 0.4973 - ETA: 0s - loss: 7.8669 - acc: 0.4975 - ETA: 0s - loss: 7.8776 - acc: 0.4970 - ETA: 0s - loss: 7.9086 - acc: 0.4952 - ETA: 0s - loss: 7.9164 - acc: 0.4948 - ETA: 0s - loss: 7.9242 - acc: 0.4942 - ETA: 0s - loss: 7.9155 - acc: 0.4947 - ETA: 0s - loss: 7.9062 - acc: 0.4955 - ETA: 0s - loss: 7.8911 - acc: 0.4962 - ETA: 0s - loss: 7.8828 - acc: 0.4969 - ETA: 0s - loss: 7.8853 - acc: 0.4969 - ETA: 0s - loss: 7.9007 - acc: 0.4958 - ETA: 0s - loss: 7.8893 - acc: 0.4967 - ETA: 0s - loss: 7.9021 - acc: 0.4960 - ETA: 0s - loss: 7.8985 - acc: 0.4963 - ETA: 0s - loss: 7.8971 - acc: 0.4965 - ETA: 0s - loss: 7.8981 - acc: 0.4965Epoch 00017: val_loss improved from 8.58997 to 8.52140, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 7.8938 - acc: 0.4969 - val_loss: 8.5214 - val_acc: 0.4084
Epoch 19/20
6640/6680 [============================>.] - ETA: 5s - loss: 9.6709 - acc: 0.4000 - ETA: 5s - loss: 7.6185 - acc: 0.5200 - ETA: 5s - loss: 7.2007 - acc: 0.5444 - ETA: 4s - loss: 7.2250 - acc: 0.5385 - ETA: 5s - loss: 7.4326 - acc: 0.5281 - ETA: 4s - loss: 7.5199 - acc: 0.5250 - ETA: 4s - loss: 7.7454 - acc: 0.5125 - ETA: 4s - loss: 7.8604 - acc: 0.5036 - ETA: 4s - loss: 7.9245 - acc: 0.5000 - ETA: 4s - loss: 8.0574 - acc: 0.4917 - ETA: 4s - loss: 8.1348 - acc: 0.4875 - ETA: 4s - loss: 8.1856 - acc: 0.4841 - ETA: 4s - loss: 8.0257 - acc: 0.4938 - ETA: 4s - loss: 7.9702 - acc: 0.4971 - ETA: 4s - loss: 7.8978 - acc: 0.5009 - ETA: 3s - loss: 7.8937 - acc: 0.5000 - ETA: 3s - loss: 7.8412 - acc: 0.5039 - ETA: 3s - loss: 7.9251 - acc: 0.4993 - ETA: 3s - loss: 7.9356 - acc: 0.4986 - ETA: 3s - loss: 7.8633 - acc: 0.5026 - ETA: 3s - loss: 7.8322 - acc: 0.5044 - ETA: 3s - loss: 7.9056 - acc: 0.4988 - ETA: 3s - loss: 7.9493 - acc: 0.4966 - ETA: 3s - loss: 7.9921 - acc: 0.4940 - ETA: 3s - loss: 8.0315 - acc: 0.4911 - ETA: 3s - loss: 8.0657 - acc: 0.4890 - ETA: 3s - loss: 8.0824 - acc: 0.4870 - ETA: 3s - loss: 8.0968 - acc: 0.4856 - ETA: 3s - loss: 8.1534 - acc: 0.4813 - ETA: 3s - loss: 8.1761 - acc: 0.4797 - ETA: 3s - loss: 8.2037 - acc: 0.4779 - ETA: 2s - loss: 8.1717 - acc: 0.4790 - ETA: 2s - loss: 8.1183 - acc: 0.4828 - ETA: 2s - loss: 8.1044 - acc: 0.4841 - ETA: 2s - loss: 8.0723 - acc: 0.4860 - ETA: 2s - loss: 8.0275 - acc: 0.4889 - ETA: 2s - loss: 7.9717 - acc: 0.4917 - ETA: 2s - loss: 7.9671 - acc: 0.4916 - ETA: 2s - loss: 7.9185 - acc: 0.4944 - ETA: 2s - loss: 7.8916 - acc: 0.4962 - ETA: 2s - loss: 7.9361 - acc: 0.4938 - ETA: 2s - loss: 7.9195 - acc: 0.4951 - ETA: 2s - loss: 7.8724 - acc: 0.4979 - ETA: 2s - loss: 7.8962 - acc: 0.4962 - ETA: 2s - loss: 7.8865 - acc: 0.4972 - ETA: 2s - loss: 7.8914 - acc: 0.4967 - ETA: 2s - loss: 7.8663 - acc: 0.4984 - ETA: 2s - loss: 7.8766 - acc: 0.4979 - ETA: 2s - loss: 7.8726 - acc: 0.4982 - ETA: 2s - loss: 7.8463 - acc: 0.5000 - ETA: 2s - loss: 7.8334 - acc: 0.5008 - ETA: 1s - loss: 7.8061 - acc: 0.5020 - ETA: 1s - loss: 7.8071 - acc: 0.5017 - ETA: 1s - loss: 7.8162 - acc: 0.5014 - ETA: 1s - loss: 7.8212 - acc: 0.5012 - ETA: 1s - loss: 7.8253 - acc: 0.5009 - ETA: 1s - loss: 7.8362 - acc: 0.5000 - ETA: 1s - loss: 7.8283 - acc: 0.5007 - ETA: 1s - loss: 7.8113 - acc: 0.5018 - ETA: 1s - loss: 7.8262 - acc: 0.5011 - ETA: 1s - loss: 7.8408 - acc: 0.5002 - ETA: 1s - loss: 7.8347 - acc: 0.5006 - ETA: 1s - loss: 7.8241 - acc: 0.5015 - ETA: 1s - loss: 7.8379 - acc: 0.5008 - ETA: 1s - loss: 7.8363 - acc: 0.5010 - ETA: 1s - loss: 7.8083 - acc: 0.5026 - ETA: 1s - loss: 7.8092 - acc: 0.5027 - ETA: 1s - loss: 7.7978 - acc: 0.5035 - ETA: 1s - loss: 7.8194 - acc: 0.5021 - ETA: 0s - loss: 7.8133 - acc: 0.5024 - ETA: 0s - loss: 7.8145 - acc: 0.5024 - ETA: 0s - loss: 7.7938 - acc: 0.5038 - ETA: 0s - loss: 7.7605 - acc: 0.5059 - ETA: 0s - loss: 7.7665 - acc: 0.5057 - ETA: 0s - loss: 7.7682 - acc: 0.5055 - ETA: 0s - loss: 7.7784 - acc: 0.5047 - ETA: 0s - loss: 7.7825 - acc: 0.5046 - ETA: 0s - loss: 7.7826 - acc: 0.5048 - ETA: 0s - loss: 7.8065 - acc: 0.5032 - ETA: 0s - loss: 7.7919 - acc: 0.5040 - ETA: 0s - loss: 7.8112 - acc: 0.5029 - ETA: 0s - loss: 7.8238 - acc: 0.5023 - ETA: 0s - loss: 7.8209 - acc: 0.5026 - ETA: 0s - loss: 7.8305 - acc: 0.5021 - ETA: 0s - loss: 7.8342 - acc: 0.5017 - ETA: 0s - loss: 7.8446 - acc: 0.5011 - ETA: 0s - loss: 7.8444 - acc: 0.5011 - ETA: 0s - loss: 7.8407 - acc: 0.5012Epoch 00018: val_loss improved from 8.52140 to 8.46115, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 7.8422 - acc: 0.5010 - val_loss: 8.4612 - val_acc: 0.4180
Epoch 20/20
6640/6680 [============================>.] - ETA: 6s - loss: 6.4482 - acc: 0.6000 - ETA: 5s - loss: 7.5760 - acc: 0.5300 - ETA: 4s - loss: 7.5431 - acc: 0.5278 - ETA: 4s - loss: 7.7091 - acc: 0.5154 - ETA: 4s - loss: 7.7916 - acc: 0.5118 - ETA: 4s - loss: 8.0151 - acc: 0.4976 - ETA: 4s - loss: 7.7344 - acc: 0.5160 - ETA: 4s - loss: 7.8838 - acc: 0.5017 - ETA: 4s - loss: 7.8303 - acc: 0.5031 - ETA: 4s - loss: 7.7220 - acc: 0.5111 - ETA: 4s - loss: 7.7006 - acc: 0.5112 - ETA: 4s - loss: 7.7332 - acc: 0.5102 - ETA: 4s - loss: 7.7908 - acc: 0.5064 - ETA: 4s - loss: 7.8300 - acc: 0.5039 - ETA: 4s - loss: 7.8760 - acc: 0.5018 - ETA: 4s - loss: 7.7937 - acc: 0.5068 - ETA: 4s - loss: 7.7850 - acc: 0.5079 - ETA: 4s - loss: 7.8256 - acc: 0.5060 - ETA: 3s - loss: 7.8308 - acc: 0.5056 - ETA: 3s - loss: 7.8107 - acc: 0.5073 - ETA: 3s - loss: 7.8236 - acc: 0.5070 - ETA: 3s - loss: 7.7780 - acc: 0.5090 - ETA: 3s - loss: 7.7913 - acc: 0.5086 - ETA: 3s - loss: 7.7862 - acc: 0.5088 - ETA: 3s - loss: 7.8120 - acc: 0.5068 - ETA: 3s - loss: 7.8021 - acc: 0.5071 - ETA: 3s - loss: 7.7536 - acc: 0.5092 - ETA: 3s - loss: 7.7425 - acc: 0.5103 - ETA: 3s - loss: 7.6749 - acc: 0.5140 - ETA: 3s - loss: 7.6326 - acc: 0.5165 - ETA: 3s - loss: 7.6526 - acc: 0.5147 - ETA: 3s - loss: 7.6738 - acc: 0.5134 - ETA: 3s - loss: 7.6804 - acc: 0.5130 - ETA: 2s - loss: 7.7231 - acc: 0.5107 - ETA: 2s - loss: 7.6918 - acc: 0.5115 - ETA: 2s - loss: 7.6681 - acc: 0.5129 - ETA: 2s - loss: 7.6485 - acc: 0.5143 - ETA: 2s - loss: 7.6348 - acc: 0.5146 - ETA: 2s - loss: 7.6303 - acc: 0.5152 - ETA: 2s - loss: 7.6581 - acc: 0.5135 - ETA: 2s - loss: 7.6845 - acc: 0.5119 - ETA: 2s - loss: 7.6742 - acc: 0.5129 - ETA: 2s - loss: 7.6937 - acc: 0.5117 - ETA: 2s - loss: 7.6992 - acc: 0.5114 - ETA: 2s - loss: 7.6779 - acc: 0.5123 - ETA: 2s - loss: 7.7202 - acc: 0.5092 - ETA: 2s - loss: 7.7101 - acc: 0.5101 - ETA: 2s - loss: 7.7090 - acc: 0.5104 - ETA: 2s - loss: 7.6888 - acc: 0.5115 - ETA: 2s - loss: 7.6772 - acc: 0.5121 - ETA: 1s - loss: 7.6546 - acc: 0.5136 - ETA: 1s - loss: 7.6906 - acc: 0.5116 - ETA: 1s - loss: 7.6978 - acc: 0.5114 - ETA: 1s - loss: 7.6981 - acc: 0.5114 - ETA: 1s - loss: 7.6917 - acc: 0.5114 - ETA: 1s - loss: 7.6693 - acc: 0.5131 - ETA: 1s - loss: 7.6678 - acc: 0.5133 - ETA: 1s - loss: 7.6800 - acc: 0.5122 - ETA: 1s - loss: 7.6990 - acc: 0.5111 - ETA: 1s - loss: 7.7158 - acc: 0.5103 - ETA: 1s - loss: 7.6988 - acc: 0.5114 - ETA: 1s - loss: 7.6955 - acc: 0.5116 - ETA: 1s - loss: 7.7093 - acc: 0.5108 - ETA: 1s - loss: 7.7202 - acc: 0.5100 - ETA: 1s - loss: 7.7543 - acc: 0.5081 - ETA: 1s - loss: 7.7500 - acc: 0.5084 - ETA: 1s - loss: 7.7691 - acc: 0.5073 - ETA: 1s - loss: 7.8085 - acc: 0.5047 - ETA: 0s - loss: 7.8043 - acc: 0.5050 - ETA: 0s - loss: 7.7935 - acc: 0.5057 - ETA: 0s - loss: 7.7995 - acc: 0.5054 - ETA: 0s - loss: 7.8032 - acc: 0.5054 - ETA: 0s - loss: 7.7956 - acc: 0.5058 - ETA: 0s - loss: 7.7824 - acc: 0.5068 - ETA: 0s - loss: 7.7784 - acc: 0.5070 - ETA: 0s - loss: 7.7717 - acc: 0.5073 - ETA: 0s - loss: 7.7728 - acc: 0.5070 - ETA: 0s - loss: 7.7777 - acc: 0.5066 - ETA: 0s - loss: 7.7841 - acc: 0.5064 - ETA: 0s - loss: 7.7696 - acc: 0.5073 - ETA: 0s - loss: 7.7879 - acc: 0.5062 - ETA: 0s - loss: 7.7817 - acc: 0.5066 - ETA: 0s - loss: 7.7752 - acc: 0.5072 - ETA: 0s - loss: 7.7827 - acc: 0.5066 - ETA: 0s - loss: 7.7788 - acc: 0.5070 - ETA: 0s - loss: 7.7746 - acc: 0.5072Epoch 00019: val_loss improved from 8.46115 to 8.41092, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 5s - loss: 7.7917 - acc: 0.5061 - val_loss: 8.4109 - val_acc: 0.4192
Out[51]:
<keras.callbacks.History at 0x23f77c7e828>

Load the Model with the Best Validation Loss

In [52]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

In [53]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Test accuracy: 42.1053%

Predict Dog Breed with the Model

In [54]:
from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

(IMPLEMENTATION) Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [55]:
### TODO: Obtain bottleneck features from another pre-trained CNN.
def get_bottleneck_features(features_file):
    """
    Generic function for getting bottleneck_features, so that I can just a filename
    and them always. This will be good if I want to dynamically choose from all
    the bottleneck_features above.
    """
    bottleneck_features = np.load(features_file)
    train = bottleneck_features['train']
    valid = bottleneck_features['valid']
    test = bottleneck_features['test']
    feature_dict = {'train':train, 'valid': valid, 'test':test}
    return feature_dict

bottleneck_features = get_bottleneck_features('bottleneck_features/DogInceptionV3Data.npz')
print('[ info ] Train Shape: ', bottleneck_features['train'].shape)
print('[ info ] Valid Shape: ', bottleneck_features['valid'].shape)
print('[ info ] Test Shape: ', bottleneck_features['test'].shape)

[ info ] Train Shape:  (6680, 5, 5, 2048)
[ info ] Valid Shape:  (835, 5, 5, 2048)
[ info ] Test Shape:  (836, 5, 5, 2048)

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer: Firsly, my architecture achieves a 76% accuracy on test which is significantly above the 60% target set by the evaluators. So its definitely suitable for the task at hand. To achieve this results for my architecture:

  • I chose the InceptionV3 pre-trained model for my transfer learning task
  • 3 Fully Connected Layers
  • A GlobalAveragePooling2D layer
  • Its four layers in total
  • To achieve this I initially tried a number of architectures. Initially I started with just a Conv2D layer and 1 Fully Connected layer. This didnt yield good results.
  • I then added a GlobalAveragePooling2D layer to the Conv2D layer and my results were below 1%!
  • I then took out the Conv2D layer and only worked with a GlobalAveragePoolind2D layer and started increasing the Fully Connected layers.
  • I then settled with the 3 Fully connected layers as they seem to give the best result at 76%.
In [56]:
### TODO: Define your architecture.

# First get the input shape here
input_shape = bottleneck_features['train'].shape[1:]
inceptionV3_model = Sequential()

#inceptionV3_model.add(Conv2D(16, (3,3), input_shape=input_shape))
#inceptionV3_model.add(Activation('relu'))
inceptionV3_model.add(GlobalAveragePooling2D(input_shape=input_shape))
#inceptionV3_model.add(GlobalAveragePooling2D())

# 3 X FULLYCONNECTED
inceptionV3_model.add(Dense(1024))
inceptionV3_model.add(Activation('relu'))
inceptionV3_model.add(Dense(512))
inceptionV3_model.add(Activation('relu'))
inceptionV3_model.add(Dense(133))
inceptionV3_model.add(Activation('softmax'))

# Print out the summary here
inceptionV3_model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_4 ( (None, 2048)              0         
_________________________________________________________________
dense_10 (Dense)             (None, 1024)              2098176   
_________________________________________________________________
activation_1146 (Activation) (None, 1024)              0         
_________________________________________________________________
dense_11 (Dense)             (None, 512)               524800    
_________________________________________________________________
activation_1147 (Activation) (None, 512)               0         
_________________________________________________________________
dense_12 (Dense)             (None, 133)               68229     
_________________________________________________________________
activation_1148 (Activation) (None, 133)               0         
=================================================================
Total params: 2,691,205.0
Trainable params: 2,691,205.0
Non-trainable params: 0.0
_________________________________________________________________

(IMPLEMENTATION) Compile the Model

In [64]:
### TODO: Compile the model.
inceptionV3_model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

Now the training

In [65]:
### TODO: Train the model.
from keras.callbacks import ModelCheckpoint  

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.InceptionV3.hdf5', 
                               verbose=1, save_best_only=True)

inceptionV3_model.fit(bottleneck_features['train'], train_targets, 
          validation_data=(bottleneck_features['valid'], valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

Train on 6680 samples, validate on 835 samples
Epoch 1/20
6660/6680 [============================>.] - ETA: 4200s - loss: 0.0037 - acc: 0.9981 - ETA: 2119s - loss: 0.0050 - acc: 0.9981 - ETA: 1425s - loss: 0.0056 - acc: 0.9977 - ETA: 1079s - loss: 0.0051 - acc: 0.9979 - ETA: 871s - loss: 0.0056 - acc: 0.9979  - ETA: 732s - loss: 0.0065 - acc: 0.9977 - ETA: 633s - loss: 0.0073 - acc: 0.9974 - ETA: 558s - loss: 0.0077 - acc: 0.9974 - ETA: 500s - loss: 0.0074 - acc: 0.9975 - ETA: 453s - loss: 0.0073 - acc: 0.9974 - ETA: 416s - loss: 0.0070 - acc: 0.9975 - ETA: 384s - loss: 0.0071 - acc: 0.9974 - ETA: 357s - loss: 0.0070 - acc: 0.9974 - ETA: 334s - loss: 0.0068 - acc: 0.9975 - ETA: 314s - loss: 0.0069 - acc: 0.9974 - ETA: 297s - loss: 0.0070 - acc: 0.9974 - ETA: 281s - loss: 0.0074 - acc: 0.9973 - ETA: 267s - loss: 0.0076 - acc: 0.9974 - ETA: 255s - loss: 0.0075 - acc: 0.9974 - ETA: 244s - loss: 0.0077 - acc: 0.9974 - ETA: 234s - loss: 0.0076 - acc: 0.9974 - ETA: 225s - loss: 0.0076 - acc: 0.9973 - ETA: 217s - loss: 0.0078 - acc: 0.9973 - ETA: 209s - 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acc: 0.9974 - ETA: 1s - loss: 0.0078 - acc: 0.9974 - ETA: 1s - loss: 0.0078 - acc: 0.9974 - ETA: 1s - loss: 0.0078 - acc: 0.9974 - ETA: 0s - loss: 0.0078 - acc: 0.9974 - ETA: 0s - loss: 0.0079 - acc: 0.9974 - ETA: 0s - loss: 0.0079 - acc: 0.9974 - ETA: 0s - loss: 0.0079 - acc: 0.9974 - ETA: 0s - loss: 0.0078 - acc: 0.9974Epoch 00000: val_loss improved from inf to 0.01000, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 74s - loss: 0.0078 - acc: 0.9974 - val_loss: 0.0100 - val_acc: 0.9968
Epoch 2/20
6660/6680 [============================>.] - ETA: 42s - loss: 0.0021 - acc: 0.9989 - ETA: 41s - loss: 0.0042 - acc: 0.9981 - ETA: 40s - loss: 0.0053 - acc: 0.9976 - ETA: 40s - loss: 0.0069 - acc: 0.9972 - ETA: 39s - loss: 0.0079 - acc: 0.9968 - ETA: 38s - loss: 0.0081 - acc: 0.9970 - ETA: 38s - loss: 0.0081 - acc: 0.9970 - ETA: 38s - loss: 0.0078 - acc: 0.9973 - ETA: 38s - loss: 0.0076 - acc: 0.9972 - ETA: 38s - loss: 0.0071 - acc: 0.9974 - ETA: 38s - loss: 0.0069 - acc: 0.9976 - ETA: 37s - loss: 0.0067 - acc: 0.9977 - ETA: 37s - loss: 0.0069 - acc: 0.9976 - ETA: 37s - loss: 0.0070 - acc: 0.9976 - ETA: 37s - loss: 0.0069 - acc: 0.9976 - ETA: 37s - loss: 0.0067 - acc: 0.9977 - ETA: 36s - loss: 0.0068 - acc: 0.9976 - ETA: 36s - loss: 0.0068 - acc: 0.9976 - ETA: 36s - loss: 0.0069 - acc: 0.9976 - ETA: 36s - loss: 0.0071 - acc: 0.9975 - ETA: 36s - loss: 0.0072 - acc: 0.9975 - ETA: 36s - loss: 0.0073 - acc: 0.9975 - ETA: 36s - loss: 0.0073 - acc: 0.9975 - ETA: 36s - loss: 0.0072 - acc: 0.9975 - 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acc: 0.9977 - ETA: 3s - loss: 0.0066 - acc: 0.9977 - ETA: 3s - loss: 0.0065 - acc: 0.9977 - ETA: 3s - loss: 0.0065 - acc: 0.9977 - ETA: 3s - loss: 0.0066 - acc: 0.9977 - ETA: 3s - loss: 0.0065 - acc: 0.9977 - ETA: 3s - loss: 0.0066 - acc: 0.9977 - ETA: 3s - loss: 0.0066 - acc: 0.9977 - ETA: 2s - loss: 0.0066 - acc: 0.9977 - ETA: 2s - loss: 0.0066 - acc: 0.9977 - ETA: 2s - loss: 0.0066 - acc: 0.9977 - ETA: 2s - loss: 0.0066 - acc: 0.9977 - ETA: 2s - loss: 0.0066 - acc: 0.9977 - ETA: 2s - loss: 0.0065 - acc: 0.9977 - ETA: 2s - loss: 0.0065 - acc: 0.9977 - ETA: 2s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 1s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977 - ETA: 0s - loss: 0.0065 - acc: 0.9977Epoch 00001: val_loss improved from 0.01000 to 0.00945, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 40s - loss: 0.0065 - acc: 0.9977 - val_loss: 0.0095 - val_acc: 0.9970
Epoch 3/20
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acc: 0.9981 - ETA: 0s - loss: 0.0053 - acc: 0.9981 - ETA: 0s - loss: 0.0053 - acc: 0.9981 - ETA: 0s - loss: 0.0053 - acc: 0.9981 - ETA: 0s - loss: 0.0053 - acc: 0.9981 - ETA: 0s - loss: 0.0053 - acc: 0.9981 - ETA: 0s - loss: 0.0053 - acc: 0.9981Epoch 00002: val_loss improved from 0.00945 to 0.00939, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 40s - loss: 0.0053 - acc: 0.9981 - val_loss: 0.0094 - val_acc: 0.9972
Epoch 4/20
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acc: 0.9985 - ETA: 0s - loss: 0.0042 - acc: 0.9985 - ETA: 0s - loss: 0.0042 - acc: 0.9985 - ETA: 0s - loss: 0.0042 - acc: 0.9985 - ETA: 0s - loss: 0.0042 - acc: 0.9985 - ETA: 0s - loss: 0.0042 - acc: 0.9985 - ETA: 0s - loss: 0.0042 - acc: 0.9985Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 40s - loss: 0.0042 - acc: 0.9985 - val_loss: 0.0104 - val_acc: 0.9969
Epoch 5/20
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6680/6680 [==============================] - 40s - loss: 0.0035 - acc: 0.9988 - val_loss: 0.0097 - val_acc: 0.9973
Epoch 6/20
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6680/6680 [==============================] - 40s - loss: 0.0034 - acc: 0.9988 - val_loss: 0.0105 - val_acc: 0.9973
Epoch 7/20
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6680/6680 [==============================] - 39s - loss: 0.0032 - acc: 0.9989 - val_loss: 0.0120 - val_acc: 0.9971
Epoch 8/20
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6680/6680 [==============================] - 40s - loss: 0.0027 - acc: 0.9991 - val_loss: 0.0107 - val_acc: 0.9972
Epoch 9/20
6660/6680 [============================>.] - ETA: 37s - loss: 8.4436e-04 - acc: 0.9996 - ETA: 39s - loss: 0.0013 - acc: 0.9996     - ETA: 39s - loss: 0.0022 - acc: 0.9992 - ETA: 39s - loss: 0.0018 - acc: 0.9994 - ETA: 39s - loss: 0.0020 - acc: 0.9993 - ETA: 39s - loss: 0.0018 - acc: 0.9994 - ETA: 39s - loss: 0.0017 - acc: 0.9995 - ETA: 39s - loss: 0.0018 - acc: 0.9994 - ETA: 38s - loss: 0.0020 - acc: 0.9993 - ETA: 38s - loss: 0.0021 - acc: 0.9993 - ETA: 38s - loss: 0.0021 - acc: 0.9992 - ETA: 37s - loss: 0.0023 - acc: 0.9992 - ETA: 37s - loss: 0.0023 - acc: 0.9992 - ETA: 37s - loss: 0.0022 - acc: 0.9992 - ETA: 36s - loss: 0.0023 - acc: 0.9992 - ETA: 36s - loss: 0.0021 - acc: 0.9992 - ETA: 36s - loss: 0.0022 - acc: 0.9992 - ETA: 36s - loss: 0.0021 - acc: 0.9992 - ETA: 36s - loss: 0.0020 - acc: 0.9992 - ETA: 35s - loss: 0.0024 - acc: 0.9992 - ETA: 35s - loss: 0.0024 - acc: 0.9991 - ETA: 35s - loss: 0.0024 - acc: 0.9991 - ETA: 35s - loss: 0.0023 - acc: 0.9992 - ETA: 35s - loss: 0.0023 - acc: 0.9992 - 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6680/6680 [==============================] - 41s - loss: 0.0027 - acc: 0.9991 - val_loss: 0.0118 - val_acc: 0.9971
Epoch 10/20
6660/6680 [============================>.] - ETA: 42s - loss: 1.5303e-04 - acc: 1.0000 - ETA: 42s - loss: 3.6887e-04 - acc: 1.0000 - ETA: 41s - loss: 0.0011 - acc: 0.9996     - ETA: 40s - loss: 0.0011 - acc: 0.9995 - ETA: 40s - loss: 9.7309e-04 - acc: 0.9996 - ETA: 39s - loss: 0.0013 - acc: 0.9994     - ETA: 39s - loss: 0.0012 - acc: 0.9995 - ETA: 39s - loss: 0.0013 - acc: 0.9995 - ETA: 38s - loss: 0.0013 - acc: 0.9995 - ETA: 38s - loss: 0.0013 - acc: 0.9995 - ETA: 38s - loss: 0.0014 - acc: 0.9995 - ETA: 38s - loss: 0.0014 - acc: 0.9995 - ETA: 38s - loss: 0.0013 - acc: 0.9995 - ETA: 38s - loss: 0.0013 - acc: 0.9995 - ETA: 38s - loss: 0.0012 - acc: 0.9995 - ETA: 37s - loss: 0.0012 - acc: 0.9996 - ETA: 37s - loss: 0.0012 - acc: 0.9995 - ETA: 37s - loss: 0.0012 - acc: 0.9995 - ETA: 37s - loss: 0.0013 - acc: 0.9995 - ETA: 37s - loss: 0.0013 - acc: 0.9995 - ETA: 37s - loss: 0.0013 - acc: 0.9995 - ETA: 37s - loss: 0.0014 - acc: 0.9995 - ETA: 37s - loss: 0.0013 - acc: 0.9995 - ETA: 36s - loss: 0.0013 - 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6680/6680 [==============================] - 41s - loss: 0.0022 - acc: 0.9992 - val_loss: 0.0121 - val_acc: 0.9971
Epoch 11/20
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6680/6680 [==============================] - 39s - loss: 0.0025 - acc: 0.9992 - val_loss: 0.0111 - val_acc: 0.9977
Epoch 12/20
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6680/6680 [==============================] - 40s - loss: 0.0015 - acc: 0.9995 - val_loss: 0.0117 - val_acc: 0.9976
Epoch 13/20
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6680/6680 [==============================] - 39s - loss: 0.0020 - acc: 0.9994 - val_loss: 0.0156 - val_acc: 0.9970
Epoch 14/20
6660/6680 [============================>.] - ETA: 38s - loss: 0.0063 - acc: 0.9977 - ETA: 38s - loss: 0.0039 - acc: 0.9987 - ETA: 38s - loss: 0.0038 - acc: 0.9987 - ETA: 38s - loss: 0.0032 - acc: 0.9989 - ETA: 38s - loss: 0.0037 - acc: 0.9989 - ETA: 38s - loss: 0.0039 - acc: 0.9989 - ETA: 38s - loss: 0.0034 - acc: 0.9990 - ETA: 38s - loss: 0.0030 - acc: 0.9992 - ETA: 38s - loss: 0.0032 - acc: 0.9992 - ETA: 37s - loss: 0.0032 - acc: 0.9992 - ETA: 38s - loss: 0.0032 - acc: 0.9992 - ETA: 38s - loss: 0.0030 - acc: 0.9992 - ETA: 37s - loss: 0.0032 - acc: 0.9991 - ETA: 37s - loss: 0.0030 - acc: 0.9991 - ETA: 37s - loss: 0.0029 - acc: 0.9991 - ETA: 37s - loss: 0.0031 - acc: 0.9991 - ETA: 37s - loss: 0.0029 - acc: 0.9992 - ETA: 37s - loss: 0.0028 - acc: 0.9992 - ETA: 36s - loss: 0.0028 - acc: 0.9992 - ETA: 36s - loss: 0.0028 - acc: 0.9992 - ETA: 36s - loss: 0.0026 - acc: 0.9992 - ETA: 36s - loss: 0.0025 - acc: 0.9993 - ETA: 36s - loss: 0.0026 - acc: 0.9993 - ETA: 36s - loss: 0.0027 - acc: 0.9992 - 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acc: 0.9994 - ETA: 0s - loss: 0.0021 - acc: 0.9994 - ETA: 0s - loss: 0.0021 - acc: 0.9994 - ETA: 0s - loss: 0.0021 - acc: 0.9994 - ETA: 0s - loss: 0.0022 - acc: 0.9994 - ETA: 0s - loss: 0.0022 - acc: 0.9994 - ETA: 0s - loss: 0.0022 - acc: 0.9994Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 38s - loss: 0.0022 - acc: 0.9994 - val_loss: 0.0126 - val_acc: 0.9972
Epoch 15/20
6660/6680 [============================>.] - ETA: 38s - loss: 0.0082 - acc: 0.9977 - ETA: 39s - loss: 0.0062 - acc: 0.9979 - ETA: 40s - loss: 0.0042 - acc: 0.9986 - ETA: 39s - loss: 0.0035 - acc: 0.9989 - ETA: 39s - loss: 0.0029 - acc: 0.9990 - ETA: 39s - loss: 0.0026 - acc: 0.9991 - ETA: 38s - loss: 0.0023 - acc: 0.9992 - ETA: 38s - loss: 0.0024 - acc: 0.9992 - ETA: 38s - loss: 0.0023 - acc: 0.9993 - ETA: 38s - loss: 0.0022 - acc: 0.9993 - ETA: 39s - loss: 0.0020 - acc: 0.9994 - ETA: 39s - loss: 0.0026 - acc: 0.9992 - ETA: 39s - loss: 0.0024 - acc: 0.9993 - ETA: 39s - loss: 0.0024 - acc: 0.9992 - ETA: 39s - loss: 0.0023 - acc: 0.9993 - ETA: 38s - loss: 0.0022 - acc: 0.9993 - ETA: 38s - loss: 0.0025 - acc: 0.9993 - ETA: 38s - loss: 0.0024 - acc: 0.9993 - ETA: 38s - loss: 0.0029 - acc: 0.9992 - ETA: 38s - loss: 0.0030 - acc: 0.9992 - ETA: 37s - loss: 0.0029 - acc: 0.9992 - ETA: 37s - loss: 0.0029 - acc: 0.9992 - ETA: 37s - loss: 0.0029 - acc: 0.9992 - ETA: 37s - loss: 0.0030 - acc: 0.9992 - 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acc: 0.9994 - ETA: 0s - loss: 0.0020 - acc: 0.9994 - ETA: 0s - loss: 0.0020 - acc: 0.9994 - ETA: 0s - loss: 0.0020 - acc: 0.9994 - ETA: 0s - loss: 0.0020 - acc: 0.9994 - ETA: 0s - loss: 0.0020 - acc: 0.9994 - ETA: 0s - loss: 0.0020 - acc: 0.9994Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 39s - loss: 0.0020 - acc: 0.9994 - val_loss: 0.0132 - val_acc: 0.9974
Epoch 16/20
6660/6680 [============================>.] - ETA: 36s - loss: 0.0034 - acc: 0.9992 - ETA: 35s - loss: 0.0025 - acc: 0.9992 - ETA: 36s - loss: 0.0029 - acc: 0.9990 - ETA: 36s - loss: 0.0023 - acc: 0.9992 - ETA: 36s - loss: 0.0030 - acc: 0.9989 - ETA: 36s - loss: 0.0026 - acc: 0.9991 - ETA: 36s - loss: 0.0029 - acc: 0.9991 - ETA: 36s - loss: 0.0028 - acc: 0.9991 - ETA: 36s - loss: 0.0026 - acc: 0.9992 - ETA: 36s - loss: 0.0024 - acc: 0.9992 - ETA: 36s - loss: 0.0023 - acc: 0.9992 - ETA: 35s - loss: 0.0025 - acc: 0.9992 - ETA: 35s - loss: 0.0024 - acc: 0.9992 - ETA: 35s - loss: 0.0023 - acc: 0.9992 - ETA: 35s - loss: 0.0021 - acc: 0.9993 - ETA: 35s - loss: 0.0020 - acc: 0.9993 - ETA: 35s - loss: 0.0019 - acc: 0.9994 - ETA: 34s - loss: 0.0019 - acc: 0.9994 - ETA: 34s - loss: 0.0020 - acc: 0.9993 - ETA: 34s - loss: 0.0019 - acc: 0.9993 - ETA: 34s - loss: 0.0021 - acc: 0.9993 - ETA: 34s - loss: 0.0020 - acc: 0.9993 - ETA: 34s - loss: 0.0020 - acc: 0.9993 - ETA: 34s - loss: 0.0019 - acc: 0.9993 - 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6680/6680 [==============================] - 39s - loss: 0.0012 - acc: 0.9996 - val_loss: 0.0128 - val_acc: 0.9975
Epoch 17/20
6660/6680 [============================>.] - ETA: 35s - loss: 6.8984e-05 - acc: 1.0000 - ETA: 36s - loss: 0.0023 - acc: 0.9992     - ETA: 35s - loss: 0.0018 - acc: 0.9995 - ETA: 36s - loss: 0.0050 - acc: 0.9991 - ETA: 36s - loss: 0.0041 - acc: 0.9992 - ETA: 36s - loss: 0.0038 - acc: 0.9992 - ETA: 36s - loss: 0.0033 - acc: 0.9994 - ETA: 36s - loss: 0.0032 - acc: 0.9992 - ETA: 35s - loss: 0.0029 - acc: 0.9993 - ETA: 35s - loss: 0.0031 - acc: 0.9993 - ETA: 35s - loss: 0.0031 - acc: 0.9993 - ETA: 35s - loss: 0.0028 - acc: 0.9994 - ETA: 35s - loss: 0.0026 - acc: 0.9994 - ETA: 35s - loss: 0.0024 - acc: 0.9995 - ETA: 35s - loss: 0.0023 - acc: 0.9995 - ETA: 35s - loss: 0.0023 - acc: 0.9995 - ETA: 34s - loss: 0.0024 - acc: 0.9994 - ETA: 34s - loss: 0.0023 - acc: 0.9994 - ETA: 34s - loss: 0.0022 - acc: 0.9994 - ETA: 34s - loss: 0.0021 - acc: 0.9995 - ETA: 34s - loss: 0.0022 - acc: 0.9994 - ETA: 34s - loss: 0.0026 - acc: 0.9994 - ETA: 34s - loss: 0.0030 - acc: 0.9994 - ETA: 34s - loss: 0.0030 - acc: 0.9994 - 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6680/6680 [==============================] - 38s - loss: 0.0016 - acc: 0.9996 - val_loss: 0.0141 - val_acc: 0.9976
Epoch 18/20
6660/6680 [============================>.] - ETA: 39s - loss: 1.0400e-04 - acc: 1.0000 - ETA: 38s - loss: 0.0011 - acc: 0.9998     - ETA: 38s - loss: 7.6613e-04 - acc: 0.9999 - ETA: 38s - loss: 6.7545e-04 - acc: 0.9999 - ETA: 37s - loss: 5.4134e-04 - acc: 0.9999 - ETA: 37s - loss: 4.9256e-04 - acc: 0.9999 - ETA: 37s - loss: 6.8556e-04 - acc: 0.9999 - ETA: 36s - loss: 7.3191e-04 - acc: 0.9998 - ETA: 36s - loss: 9.8549e-04 - acc: 0.9997 - ETA: 36s - loss: 0.0014 - acc: 0.9997     - ETA: 36s - loss: 0.0015 - acc: 0.9997 - ETA: 36s - loss: 0.0014 - acc: 0.9996 - ETA: 35s - loss: 0.0013 - acc: 0.9997 - ETA: 35s - loss: 0.0013 - acc: 0.9997 - ETA: 35s - loss: 0.0013 - acc: 0.9996 - ETA: 35s - loss: 0.0013 - acc: 0.9996 - ETA: 35s - loss: 0.0012 - acc: 0.9997 - ETA: 35s - loss: 0.0011 - acc: 0.9997 - ETA: 34s - loss: 0.0011 - acc: 0.9997 - ETA: 34s - loss: 0.0010 - acc: 0.9997 - ETA: 34s - loss: 9.8888e-04 - acc: 0.9997 - ETA: 34s - loss: 9.6708e-04 - acc: 0.9997 - ETA: 34s - loss: 9.5661e-04 - 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acc: 0.9997 - ETA: 1s - loss: 0.0011 - acc: 0.9997 - ETA: 1s - loss: 0.0011 - acc: 0.9997 - ETA: 1s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997 - ETA: 0s - loss: 0.0011 - acc: 0.9997Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 39s - loss: 0.0011 - acc: 0.9997 - val_loss: 0.0156 - val_acc: 0.9973
Epoch 19/20
6660/6680 [============================>.] - ETA: 36s - loss: 1.5573e-04 - acc: 1.0000 - ETA: 36s - loss: 0.0032 - acc: 0.9992     - ETA: 36s - loss: 0.0063 - acc: 0.9992 - ETA: 36s - loss: 0.0052 - acc: 0.9992 - ETA: 35s - loss: 0.0042 - acc: 0.9994 - ETA: 36s - loss: 0.0047 - acc: 0.9992 - ETA: 35s - loss: 0.0054 - acc: 0.9991 - ETA: 35s - loss: 0.0050 - acc: 0.9992 - ETA: 35s - loss: 0.0049 - acc: 0.9990 - ETA: 35s - loss: 0.0051 - acc: 0.9991 - ETA: 35s - loss: 0.0046 - acc: 0.9992 - ETA: 35s - loss: 0.0043 - acc: 0.9992 - ETA: 35s - loss: 0.0041 - acc: 0.9992 - ETA: 34s - loss: 0.0038 - acc: 0.9993 - ETA: 34s - loss: 0.0037 - acc: 0.9993 - ETA: 35s - loss: 0.0035 - acc: 0.9992 - ETA: 35s - loss: 0.0033 - acc: 0.9993 - ETA: 35s - loss: 0.0032 - acc: 0.9993 - ETA: 34s - loss: 0.0033 - acc: 0.9993 - ETA: 34s - loss: 0.0031 - acc: 0.9993 - ETA: 34s - loss: 0.0032 - acc: 0.9993 - ETA: 34s - loss: 0.0030 - acc: 0.9994 - ETA: 34s - loss: 0.0034 - acc: 0.9993 - ETA: 34s - loss: 0.0036 - acc: 0.9993 - 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6680/6680 [==============================] - 38s - loss: 0.0019 - acc: 0.9995 - val_loss: 0.0150 - val_acc: 0.9974
Epoch 20/20
6660/6680 [============================>.] - ETA: 37s - loss: 1.4658e-05 - acc: 1.0000 - ETA: 37s - loss: 5.2196e-04 - acc: 0.9996 - ETA: 37s - loss: 3.7375e-04 - acc: 0.9997 - ETA: 36s - loss: 2.8825e-04 - acc: 0.9998 - ETA: 36s - loss: 2.4723e-04 - acc: 0.9998 - ETA: 36s - loss: 7.3806e-04 - acc: 0.9997 - ETA: 36s - loss: 7.2386e-04 - acc: 0.9997 - ETA: 36s - loss: 0.0014 - acc: 0.9994     - ETA: 35s - loss: 0.0019 - acc: 0.9993 - ETA: 35s - loss: 0.0017 - acc: 0.9994 - ETA: 35s - loss: 0.0016 - acc: 0.9994 - ETA: 35s - loss: 0.0016 - acc: 0.9993 - ETA: 35s - loss: 0.0015 - acc: 0.9994 - ETA: 35s - loss: 0.0019 - acc: 0.9994 - ETA: 35s - loss: 0.0022 - acc: 0.9993 - ETA: 34s - loss: 0.0021 - acc: 0.9994 - ETA: 35s - loss: 0.0021 - acc: 0.9993 - ETA: 35s - loss: 0.0028 - acc: 0.9993 - ETA: 35s - loss: 0.0029 - acc: 0.9993 - ETA: 35s - loss: 0.0030 - acc: 0.9993 - ETA: 35s - loss: 0.0031 - acc: 0.9993 - ETA: 35s - loss: 0.0029 - acc: 0.9993 - ETA: 35s - loss: 0.0029 - acc: 0.9993 - ETA: 34s - 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6680/6680 [==============================] - 38s - loss: 0.0022 - acc: 0.9993 - val_loss: 0.0154 - val_acc: 0.9973
Out[65]:
<keras.callbacks.History at 0x241214db6d8>

(IMPLEMENTATION) Load the Model with the Best Validation Loss

In [66]:
### TODO: Load the model weights with the best validation loss.
inceptionV3_model.load_weights('saved_models/weights.best.InceptionV3.hdf5')

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

In [67]:
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(inceptionV3_model.predict(np.expand_dims(feature, axis=0))) for feature in bottleneck_features['test']]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Test accuracy: 76.1962%

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [68]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
from extract_bottleneck_features import *

def inceptionV3_predict_breed(img_path):
    # extract bottleneck features corresponding to the InceptionV3 CNN model
    bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
    
    # Supply bottleneck_feature as input to the InceptionV3 model to get the
    # predicted vector
    predicted_vector = inceptionV3_model.predict(bottleneck_feature)
    
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [71]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
import os.path
def show_image(img_path):
    # First if the path is actually valid
    assert(os.path.isfile(img_path) == True)
    
    # Load colour (RGB) image
    img = cv2.imread(img_path)
    
    # convert BGR image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # extract pre-trained face detector
    face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
    
    # find faces in image
    faces = face_cascade.detectMultiScale(gray)
    
    # print number of faces detected in the image
    if len(faces) > 1:
        print('Multiple faces detected, only one will classified:', len(faces))
    
    # get bounding box for each detected face
    for (x,y,w,h) in faces:
        # add bounding box to color image
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
    # convert BGR image to RGB for plotting
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    # display the image, along with bounding box
    plt.imshow(cv_rgb)
    
    # Show it
    plt.show()

# Human face detector
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

# Dog detector
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

def detect_and_display(img_path):
    
    # Check if we get a human, dog or neither
    if not face_detector(img_path) and not dog_detector(img_path):
        print("No Dog or Human was detected in this image.")
        show_image(img_path)
        print("Please enter a dog or human picture!")
    else:
        if dog_detector(img_path):
            print("Hello, Dawg!")
            
            # Show the image
            show_image(img_path)
            
            # Show the breed
            print("Your breed is a ", inceptionV3_predict_breed(img_path))
            
        else:
            print("Hello, Human!")
            
            # Show image
            show_image(img_path)
            
            # Show the resembling dog breed for this human
            print("The dog you resemble is a ", inceptionV3_predict_breed(img_path))

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: Given the 76% accuracy I achieved above my output below perform as expected. Some improvements to my algorithm might include:

  • I have used the default 20 epochs hyperparameter. I could have used GridSearchCV from scikit-learn to tune this parameter for better results;
  • I have also used the default batch_size of 20 and I could have used GridSearchCV from scikit-learn to tune this paramter for better performance;
  • I havent had time to perform data augmentation, I think this could improve the accuracy;
  • My algorithm as it stands can not deal with multiple images on a picture. For example, below I tested an image containing people and a dog and it only picked out the dog - it could pick a person on a different image, I am not sure;
  • The algorithm can also not deal with multiple dog breeds on the same picture. I entered a picture with two different dog breeds on the picture and it only picked out one dog;
In [72]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
import os
dirpath = os.getcwd() + "\images"
for root, directories, filenames in os.walk(dirpath):
    for filename in filenames:
        full_path = os.path.join(root, filename)
        detect_and_display(full_path)
    

Hello, Human!

The dog you resemble is a  Dachshund
No Dog or Human was detected in this image.

Please enter a dog or human picture!
Hello, Dawg!

Your breed is a  Cocker_spaniel
Hello, Human!

The dog you resemble is a  Dachshund
Hello, Dawg!

Your breed is a  Brittany
Hello, Dawg!

Your breed is a  Bulldog
Hello, Dawg!

Your breed is a  Curly-coated_retriever
Hello, Dawg!

Your breed is a  Brussels_griffon
Hello, Human!

The dog you resemble is a  Dachshund
No Dog or Human was detected in this image.

Please enter a dog or human picture!
Hello, Dawg!

Your breed is a  Norwegian_buhund
Hello, Human!

The dog you resemble is a  Dachshund
Hello, Human!

The dog you resemble is a  English_setter
Hello, Dawg!
Multiple faces detected, only one will classified: 2

Your breed is a  Golden_retriever
Hello, Dawg!

Your breed is a  Flat-coated_retriever
Hello, Dawg!

Your breed is a  Chesapeake_bay_retriever
Hello, Dawg!

Your breed is a  Labrador_retriever
Hello, Human!

The dog you resemble is a  Bernese_mountain_dog
No Dog or Human was detected in this image.

Please enter a dog or human picture!
No Dog or Human was detected in this image.

Please enter a dog or human picture!
Hello, Human!

The dog you resemble is a  English_setter
Hello, Dawg!

Your breed is a  German_shepherd_dog
Hello, Dawg!

Your breed is a  Boxer
Hello, Dawg!

Your breed is a  Irish_red_and_white_setter
Hello, Human!

The dog you resemble is a  Smooth_fox_terrier
In [ ]: